fix(visible): safe-inpaint pill gate, cut metadata-only false fires (#58)

Verified 0.13.0 pill removal on a 32k real-upload corpus. The metadata-OR-wordmark
gate was only ~1/3 precise: TC260 metadata confirms Jimeng-class provenance, not pill
presence, so the weak edge-NCC detector's false fires (textured ceilings/walls, where
inpaint visibly smears) were admitted whenever metadata was present.

Split into two arms (_keep_pill): the reliable bottom-right wordmark (~94% precise,
survives metadata stripping) removes the pill unrestricted; the metadata-only arm
removes it ONLY when the top-left footprint is flat enough for an invisible inpaint
(PillEngine.footprint_is_flat, median-Sobel <= _FLAT_TEXTURE_MAX). Keeps real
flat-scene pills and harmless flat false fires; leaves the damaging textured false
fires untouched. Corpus: 270 -> 118 removals, ~90 true preserved, damaging FP -> ~0.

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

.claude/settings.json

@@ -0,0 +1,14 @@
+{
+  "permissions": {
+    "allow": [
+      "WebSearch",
+      "WebFetch"
+    ]
+  },
+  "enabledPlugins": {
+    "pyright-lsp@claude-plugins-official": true,
+    "context7@claude-plugins-official": true,
+    "code-simplifier@claude-plugins-official": true,
+    "claude-md-management@claude-plugins-official": true
+  }
+}

.github/workflows/distribute.yml

@@ -67,7 +67,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout the tap
-        uses: actions/checkout@v6
+        uses: actions/checkout@v7
         with:
           repository: wiltodelta/homebrew-tap
           token: ${{ secrets.HOMEBREW_TAP_TOKEN }}

.github/workflows/publish.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     environment: pypi
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
 
       - uses: astral-sh/setup-uv@v7
 

.github/workflows/test.yml

@@ -13,7 +13,7 @@ jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
       - uses: astral-sh/setup-uv@v7
       - name: Sync dev environment
         run: uv sync --frozen --extra dev
@@ -36,7 +36,7 @@ jobs:
         os: [ubuntu-latest, macos-latest, windows-latest]
         python-version: ["3.10", "3.12"]
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
       - uses: astral-sh/setup-uv@v7
         with:
           python-version: ${{ matrix.python-version }}

CLAUDE.md

@@ -17,21 +17,23 @@ Consequences for contributors (do not drift back into the stock niche just becau
 
 ## How to run
 
-- `uv run remove-ai-watermarks all <image.png> -o <output.png>` — full pipeline (visible + invisible + metadata). Same diffusion knobs as `invisible` below, plus the visible-pass `--inpaint/--no-inpaint`/`--inpaint-method`. **When the `[gpu]` extra is absent, step 2 (invisible/SynthID) is skipped** — `all` still writes an output (visible mark + metadata stripped) but prints a prominent end-of-run banner ("the invisible (SynthID) watermark was NOT removed") AND exits **non-zero** (1), so a skipped SynthID pass is not mistaken for a clean result (the recurring #14/#47 trap, where the old quiet inline warning was missed). `invisible` already hard-errors without the extra; only `all` continued, hence the loud end-banner. Regression-guarded by `tests/test_cli.py::TestAllCommand::test_all_loud_warning_and_nonzero_exit_when_gpu_missing`. **No-signal skip (P0#5):** step 2 also runs the same `has_invisible_target` gate (see `invisible` below) — when no invisible watermark is detectable and `--force` is not set, step 2 is skipped and the pixels are left intact, but unlike the GPU-missing skip this is a **SUCCESS (exit 0)**: the visible pass + metadata strip still ran and a file is written (the message says so without claiming the image is clean). Distinct exit semantics by design: GPU-missing = couldn't do the work (non-zero); no-signal = nothing to do (zero). Regression-guarded by `test_all_skips_invisible_on_no_signal_but_succeeds`. **Test trap:** any `all` test that exercises the full pipeline MUST `patch("remove_ai_watermarks.invisible_engine.is_available", return_value=True)` — CI installs core+dev only (no `[gpu]`), so an unpatched `all` test takes the skip branch and now hits the non-zero exit. This passed locally (gpu present → `is_available()` True) but red-failed every matrix cell on the v0.11.0 commit (`test_all_basic`/`test_all_visible_step_uses_registry` asserted exit 0); both now patch `is_available` True.
-- `uv run remove-ai-watermarks invisible <image.png> -o <out.png>` — diffusion SynthID removal. **Full knob set** (kept identical across `invisible`/`all`/`batch`): `--strength` (vendor-adaptive default), `--steps`, `--guidance-scale` (CFG, default 7.5), `--pipeline sdxl|controlnet|qwen` (default `controlnet`; `qwen` is a manual opt-in only — see the qwen note in the module map), `--controlnet-scale`, `--model` (HF model id, default SDXL base), `--device`, `--seed`, `--hf-token`, `--max-resolution`/`--min-resolution`, `--upscaler lanczos|esrgan`, `--humanize` (Analog Humanizer grain), `--unsharp` (final sharpen), `--adaptive-polish/--no-adaptive-polish` (**ON by default**; detail-targeted polish that self-gates to a no-op where there is no deficit), and `--tile/--no-tile` + `--tile-size`/`--tile-overlap` (**OFF by default**; sliding-window tiled diffusion -- the *lossless* alternative to a `--max-resolution` downscale for large inputs that OOM on MPS/GPU. Engages only when the long side exceeds `--tile-size`, default 1024; tiles are feather-blended over `--tile-overlap` px, default 128. Pair with `--max-resolution 0`). `--auto` is deprecated and now a no-op that only warns (the polish it used to enable is ON by default). **No-signal skip (P0#5, roadmap):** before the diffusion runs, the command checks `identify.has_invisible_target(source)` (the `ProvenanceReport.ai_from_metadata` union: C2PA AI-issuer / SynthID proxy, IPTC, AIGC, local gen params, EXIF/xAI, open DWT-DCT / TrustMark — visible marks do NOT count, they are a separate pass). When nothing is locally detectable it does NOT regenerate (that would only degrade a clean image — the dominant paid score-0 cause on no-watermark uploads): it writes NO output, prints guidance that does NOT claim the image is clean (a pixel SynthID is undetectable once its metadata proxy is gone), and exits **`EXIT_NO_INVISIBLE_SIGNAL` (2)** — same value/role as the visible `EXIT_NO_VISIBLE_MARK`. `--force/--no-force` (**default skip = ON**) runs the scrub regardless. The check fails SAFE (a detector exception → run, since leaving a watermark on a paid removal is worse than over-regenerating). Helpers `cli._no_invisible_signal_exit` + `identify.has_invisible_target`; regression-guarded by `tests/test_cli.py::TestInvisibleCommand::{test_invisible_no_signal_skips_and_exits_two,test_invisible_force_runs_scrub_on_no_signal,test_invisible_runs_without_force_when_signal_present}` and `tests/test_identify.py::TestHasInvisibleTargetFailSafe`. **Test trap:** any `invisible`/`all`/`batch` test that exercises the diffusion path on a signal-LESS fixture (e.g. the synthetic `sample_png`) MUST pass `--force`, or the new gate skips step 2 (so `mock_engine.remove_watermark` is never called / `invisible` exits 2).
-- `uv run remove-ai-watermarks visible <image.png> -o <out.png>` — known-visible-mark removal, CPU, no GPU. Reverse-alpha based: each mark is removed by inverting its captured alpha map. `--mark auto` (default) picks the strongest detected of the Gemini sparkle, the Doubao "豆包AI生成" text strip, the Jimeng "★ 即梦AI" wordmark, and the Samsung Galaxy AI "✦ Contenuti generati dall'AI" strip (bottom-LEFT, locale-specific — Italian variant calibrated); `--mark gemini` / `--mark doubao` / `--mark jimeng` / `--mark samsung` force one (choices come from the registry). Gemini/Doubao recover pixels exactly with no inpaint at native; **Jimeng and Samsung add an always-on thin residual inpaint over the glyph footprint** (their marks re-rasterize per image, so reverse-alpha alone leaves a faint outline). For arbitrary logos/objects use `erase`. **When `--mark auto` finds no known mark (the common case — ~74% of real uploads carry no registered visible mark), the command does NOT silently re-serve the input as a finished result.** It runs a cheap metadata-only `identify`, prints actionable guidance (if the image carries an invisible/metadata mark, e.g. an OpenAI/Gemini C2PA image, it points to `all`; otherwise it does NOT imply the image is clean -- it warns that an invisible pixel watermark like SynthID cannot be detected once the metadata proxy is gone and routes to both `all` and `erase --region`), writes NO output file, and exits **`EXIT_NO_VISIBLE_MARK` (2)** — distinct from success (0) and a hard error (1) so a wrapping service (raiw.cc) can surface the message instead of treating the unchanged image as done (the production "it didn't work" / score-0 trap). Same handling for an explicit `--mark <name>` that is not detected. Helper `cli._no_visible_mark_exit`; regression-guarded by `tests/test_cli.py::TestVisibleCommand::test_visible_auto_no_mark_exits_two_with_eraser_hint` and `test_visible_auto_no_mark_routes_to_all_when_metadata`. `--no-detect` still forces the gemini fallback and proceeds (exit 0).
-- `uv run remove-ai-watermarks erase <image.png> --region x,y,w,h -o <out.png>` — universal region eraser (any logo/object, any position). `--backend cv2` (default, no deps) or `--backend lama` (big-LaMa via onnxruntime, extra `lama`); `--region` is repeatable.
+Per-command exit-code semantics (the no-signal / GPU-missing skip branches), test traps, and regression-guard paths live in `docs/module-internals.md` (section "CLI commands (`cli.py`)") — read it before changing any command's skip/exit behavior.
+
+- `uv run remove-ai-watermarks all <image.png> -o <output.png>` — full pipeline (visible + invisible + metadata). Same diffusion knobs as `invisible`, plus the visible-pass `--inpaint/--no-inpaint`/`--inpaint-method`. Skips step 2 (invisible/SynthID) when the `[gpu]` extra is absent or no invisible signal is detectable; see the module doc for the distinct exit codes.
+- `uv run remove-ai-watermarks invisible <image.png> -o <out.png>` — diffusion SynthID removal. **Full knob set** (kept identical across `invisible`/`all`/`batch`): `--strength` (vendor-adaptive default), `--steps`, `--guidance-scale` (CFG, default 7.5), `--pipeline sdxl|controlnet|qwen` (default `controlnet`; `qwen` is a manual opt-in only — see the qwen note in the module map), `--controlnet-scale`, `--model` (HF model id, default SDXL base), `--device`, `--seed`, `--hf-token`, `--max-resolution`/`--min-resolution`, `--upscaler lanczos|esrgan`, `--humanize` (Analog Humanizer grain), `--unsharp` (final sharpen), `--adaptive-polish/--no-adaptive-polish` (**ON by default**), `--tile/--no-tile` + `--tile-size`/`--tile-overlap` (**OFF by default**), `--force/--no-force` (default skip = ON, runs the scrub even with no detected signal). `--auto` is deprecated and a no-op that only warns. Skips the diffusion when no invisible signal is detectable (the no-signal gate); see the module doc.
+- `uv run remove-ai-watermarks visible <image.png> -o <out.png>` — known-visible-mark removal. `--method auto` (default) uses reverse-alpha for the capture marks (recovers exact pixels, CPU, no GPU -- measured cleaner + lighter than inpaint on them) and inpaint only for the capture-less pill; `--method reverse-alpha|inpaint` forces one (inpaint uses MI-GAN with the `migan` extra, else cv2). `--mark auto` (default) removes EVERY detected mark in one pass (a Jimeng-basic image carries the top-left "AI生成" pill AND the bottom-right "★ 即梦AI" wordmark) from: Gemini sparkle, Doubao "豆包AI生成", Jimeng "★ 即梦AI", Samsung Galaxy AI "✦ Contenuti generati dall'AI", and the capture-less Jimeng "AI生成" pill (top-left, metadata-gated); `--mark gemini|doubao|jimeng|samsung|jimeng_pill` forces one. For arbitrary logos/objects use `erase`. When no known mark is detected the command writes no output and exits with the no-visible-mark code instead of re-serving the input; `--no-detect` forces the gemini fallback and proceeds. See the module doc for the routing/exit detail. `--method` is shared across `visible`/`all`/`batch`.
+- `uv run remove-ai-watermarks erase <image.png> --region x,y,w,h -o <out.png>` — universal region eraser (any logo/object, any position). `--backend cv2` (default, no deps), `--backend migan` (MI-GAN via onnxruntime, extra `migan`; ~28 MB, ~1 GB RAM, near-LaMa), or `--backend lama` (big-LaMa, extra `lama`; best quality but ~4.7 GB RAM); `--region` is repeatable.
 - `uv run remove-ai-watermarks identify <image>` — provenance verdict (platform + watermark inventory + confidence); `--json` for machine output, `--no-visible` to skip the cv2 sparkle detector
 - `uv run remove-ai-watermarks metadata <image.png> --check` — inspect AI metadata (C2PA, EXIF, PNG chunks)
 - `uv run remove-ai-watermarks metadata <image.png> --remove -o <out.png>` — strip all AI metadata
-- `uv run remove-ai-watermarks batch <directory>` — process every supported image in a directory (output defaults to `<directory>_clean/`, set with `-o`). `--mode visible|invisible|metadata|all` (default `visible`); the invisible/all path reuses the **full `invisible` knob set above** (`--strength`/`--steps`/`--guidance-scale`/`--pipeline`/`--controlnet-scale`/`--model`/`--device`/`--max-resolution`/`--min-resolution`/`--upscaler`/`--seed`/`--hf-token`/`--humanize`/`--unsharp`/`--adaptive-polish`/`--tile`/`--tile-size`/`--tile-overlap`/`--force`), plus `--inpaint/--no-inpaint` for the visible pass. `--adaptive-polish` is ON by default; `--auto` is deprecated and a no-op that only warns. **No-signal skip (P0#5):** in invisible/all mode each image runs the same `has_invisible_target` gate — a signal-less image is skipped (no diffusion); in `invisible` mode the input is copied through to the output dir so it stays complete, in `all` mode the visible-removed result is kept and metadata is still stripped. `--force` scrubs every image regardless. One engine cached per pipeline; the polish is resolved once before the loop.
+- `uv run remove-ai-watermarks batch <directory>` — process every supported image in a directory (output defaults to `<directory>_clean/`, set with `-o`). `--mode visible|invisible|metadata|all` (default `visible`); the invisible/all path reuses the full `invisible` knob set above, plus `--inpaint/--no-inpaint` for the visible pass. Applies the same no-signal skip per image; see the module doc.
 
 ## Test and lint
 
 - **CI** (`.github/workflows/test.yml`): runs on push to `main` + every PR. A `lint` job (ubuntu: `ruff check` + `ruff format --check`) plus a `test` matrix (ubuntu/macos/windows x py3.10/3.12) that does `uv sync --frozen --extra dev` then `pytest`. The matrix installs only core + dev (no `gpu` extra), so the GPU/model-running tests skip there and it exercises the metadata/identify/visible/cv2-eraser surface on all three OSes. Keep `uv.lock` valid (don't break `--frozen`) when editing `pyproject.toml`.
 - **Release flow + distribution channels** (PyPI publish via `publish.yml`/`uv publish`, the automated Homebrew-tap + HF-Space bumps in `distribute.yml`, conda-forge, ComfyUI Registry, the sdist `data/` exclusion, hatchling pin history): see `docs/release-and-distribution.md` before cutting a release.
 - `bash maintain.sh` — uv-outdated, uv-secure, ruff check/fix, ruff format, pyright (scoped `src/`, see the OOM note below), pytest -n auto. The helper tools live in the `dev` extra (`pytest-xdist`, plus `uv-outdated`/`uv-secure` marker-gated to py3.12+ so the py3.10 resolution stays solvable) — a bare env without `--extra dev` does not have them.
-- **Strict pyright is clean across `src/` (0 errors).** The cv2/torch/diffusers boundary files (`gemini_engine`, `region_eraser`, `doubao_engine`, `humanizer`, `invisible_engine`, `noai/watermark_remover`) carry a documented per-file `# pyright:` relax pragma that turns off only the unknown-type / untyped-third-party rules — those libs ship no usable types, so strict typing there fights the ecosystem. Pure-logic files stay fully strict; `typings/piexif/__init__.pyi` is a local stub so `metadata.py`/`extractor.py` resolve piexif. Public ndarray-returning signatures on the relaxed engines are still annotated `NDArray[Any]` so strict consumers (`cli.py`) stay clean. When touching a relaxed file, prefer fixing real issues over widening the pragma; keep the pragma scoped to genuinely-untyped boundaries. (`uv-secure` is clean since idna was bumped 3.11 -> 3.16, fixing GHSA-65pc-fj4g-8rjx, and aiohttp 3.13.5 -> 3.14.0 via `uv lock --upgrade-package aiohttp`, fixing GHSA-hg6j-4rv6-33pg + GHSA-jg22-mg44-37j8. (The old basicsr Dependabot alert (GHSA-86w8-vhw6-q9qq) is resolved by removal: the experimental `restore` extra was retired and basicsr is no longer anywhere in the dependency tree.) The torch Dependabot alert **GHSA-rrmf-rvhw-rf47** (`torch.jit.script` memory corruption, vulnerable `<= 2.12.0`) is **dismissed as `not_used`** (2026-06-10): torch is a transitive dep of the optional `gpu` extra only, the codebase never calls `torch.jit` (grep-verified), and **no patched torch version exists** (`first_patched_version` is null), so it cannot be closed by an upgrade — do not re-triage it.
+- **Strict pyright is clean across `src/` (0 errors).** The cv2/torch/diffusers boundary files (`gemini_engine`, `region_eraser`, `doubao_engine`, `humanizer`, `invisible_engine`, `noai/watermark_remover`) carry a documented per-file `# pyright:` relax pragma that turns off only the unknown-type / untyped-third-party rules — those libs ship no usable types, so strict typing there fights the ecosystem. Pure-logic files stay fully strict; `typings/piexif/__init__.pyi` is a local stub so `metadata.py`/`extractor.py` resolve piexif. Public ndarray-returning signatures on the relaxed engines are still annotated `NDArray[Any]` so strict consumers (`cli.py`) stay clean. When touching a relaxed file, prefer fixing real issues over widening the pragma; keep the pragma scoped to genuinely-untyped boundaries. The `uv-secure` CVE-resolution history (idna/aiohttp bumps, retired basicsr, the dismissed torch `GHSA-rrmf-rvhw-rf47`) lives in `docs/release-and-distribution.md` — read it before re-triaging a dependency alert.
 - **Full-project `uv run pyright` (no path) OOMs/crashes node on this ML-heavy repo** (emits a `libnode` stack frame, no summary) — a known environment limit, not a code error. Gate with `uv run --extra dev --extra gpu pyright src/` (completes, authoritative) or scope to changed files; also run `uv run ruff check` and `uv run pytest` directly.
 - Run `uv run` from the repo root — from another cwd it falls back to a bare env without numpy/cv2/torch.
 - **Stale `trustmark` remnant in site-packages after an extras change:** the `trustmark` package downloads model weights INTO its own package dir, so when a narrower `uv sync` prunes the package, a `trustmark/models/` directory survives as an empty namespace package. Symptom: pyright `"TrustMark" is unknown import symbol` on `trustmark_detector.py` and `find_spec("trustmark")` returning a loader-less spec (so `is_available()` lies True). Fix: `rm -rf .venv/lib/python3.12/site-packages/trustmark` (regenerable weights cache).
@@ -54,11 +56,12 @@ Compact map. The full per-module detail (design decisions, tuned thresholds, cal
 - `noai/constants.py` — the single `C2PA_AI_VENDORS` registry (+ `C2PA_SOFT_BINDINGS`) from which `C2PA_ISSUERS` / `SYNTHID_C2PA_ISSUERS` / `identify._ISSUER_PLATFORM` are all derived. Add a new vendor as one registry entry; never edit the derived dicts and never add inline.
 - `metadata.py` — `scan_head(path)` is the shared (memoized) input for every C2PA/AIGC/IPTC byte scan; use it instead of `open().read(1MB)` for any new marker scan. Also home to `synthid_source`, `xai_signature`, `iptc_ai_system`, `aigc_label`, `huggingface_job`, `samsung_genai`, and `remove_ai_metadata` (fail-safe `strip_c2pa_boxes`). `exif_generator` matches a VALUE against `AI_GENERATOR_TOKENS` across EXIF `Software`/`Make`/`Artist`/`ImageDescription`, XMP `CreatorTool`, AND PNG `tEXt` chunks (`Software`/`Source`/`Title`/`Description` — NovelAI stamps there, not EXIF). **Detection and removal must stay in parity:** a generator that stamps an AI-shaped VALUE under a non-AI KEY (NovelAI's `Title`/`Source`) is dropped on removal by `_is_ai_value` (value-token match, mirrors `exif_generator`), NOT by `_is_ai_key` alone — else the cleaned file still reads as that generator. Add a new no-C2PA generator = one `AI_GENERATOR_TOKENS` entry (use a distinctive token, e.g. `reve.com` not bare `reve`); detection and removal then both follow. Regression: `tests/test_metadata.py::TestExifGenerator::{test_novelai_png_text_chunk_detected,test_novelai_removal_parity}`.
 - `identify.py` — aggregates every locally-readable signal into one `ProvenanceReport`; `is_ai_generated` is True or None, never asserted False. `ProvenanceReport.ai_source_kind` exposes the C2PA digital-source-type split — `"generated"` (trainedAlgorithmicMedia, fully AI) vs `"enhanced"` (compositeWithTrainedAlgorithmicMedia, a real photo with an AI-composited region), else None — so a caller branches full-frame scrub vs region-targeted clean (see `noai/tiling.feather_region_composite` + `WatermarkRemover.remove_watermark(region=...)`). The sparkle provenance threshold is the SHARED `watermark_registry.GEMINI_SPARKLE_TRUST_CONF` (imported, not a private copy) so the provenance "is there a sparkle" verdict and the removal "take the sparkle" decision can never drift. `import identify` is deliberately light (lazy `noai/__init__`, fits a 512 MB host) — keep heavy imports out (the `watermark_registry` constant import stays light: engines are lazy there). Add capture-camera tokens to `_DEVICE_C2PA_PLATFORM` only when verified against a real C2PA file; editing-app/AI-device signer tokens go to `_SIGNER_C2PA_PLATFORM`; generator/issuer platforms to `C2PA_AI_VENDORS` in `constants.py`. Integrity-clash detection is high-precision by design (only hard generator stamps feed it, source-grouped independence).
-- `watermark_registry.py` — the single catalog of known visible watermarks (gemini / doubao / jimeng / samsung), reverse-alpha based by policy. Add a new visible text mark = one `_text_mark(...)` row + a `TextMarkConfig` with a captured alpha map; do not re-add per-mark `if` branches. `cli._write_bgr_with_alpha` must NOT zero alpha in the watermark bbox (issue #30 white-box regression). **A new visible mark is HARD-BLOCKED on real controlled flat captures** — the alpha map is solved by `scripts/visible_alpha_solve.py` from solid black + gray (+white) captures of the mark produced by the actual app/device at native resolution, committed under `data/<engine>_capture/captures/`. Do NOT synthesize the alpha by font-rendering the wordmark — the user rejected synthetic reconstruction as below the quality bar (2026-06-22), and per-image alpha falloff/variation needs the real capture. Corpus images are NOT a substitute (data-safety: no corpus-derived committed assets; and reverse-alpha needs the flat capture, not real content). So if no flat capture exists for a mark, the work is parked — do not propose synthetic, do not derive from user uploads. (Meta AI, more Samsung locales, and any Grok visible mark are all parked on this; Grok additionally needs confirming it even HAS a visible mark — its known signal is EXIF-only `xai_signature`.)
+- `watermark_registry.py` — the single catalog of known visible watermarks (gemini / doubao / jimeng / samsung / jimeng_pill). **`--mark auto` removes EVERY detected mark in one pass** via `remove_auto_marks` (marks coexist -- a Jimeng-basic image has the top-left pill AND the bottom-right wordmark; `best_auto_mark` single-strongest would leave one). **The `jimeng_pill` is CAPTURE-LESS** (`has_capture=False`, `pill_engine.py`): the top-left "AI生成" label has no alpha map, so it is detect-by-synthetic-silhouette + inpaint-only (every method resolves to inpaint). Its weak edge-NCC detector (~7% raw false-fire) is gated in `remove_auto_marks` via **`_keep_pill`** (32k real-upload corpus validation 2026-07): the pill never rides on a **Doubao** detection, and has **two confirmation arms** because metadata confirms the platform, not pill presence. **(1) Bottom-right "★ 即梦AI" wordmark fired** — ~94% precise and survives **metadata-STRIPPED uploads** (screenshots / re-saves, ~61% of pills carry a detectable wordmark): remove **unrestricted**. **(2) TC260 metadata only** (CLI `_aigc_metadata_present` → `pill_metadata`, no wordmark) — only ~27% precise, its false fires are **textured ceilings/walls that inpaint visibly SMEARS**, so remove **only when the top-left footprint is flat enough for an invisible inpaint** (`pill_engine.footprint_is_flat`, median-Sobel texture ≤ `_FLAT_TEXTURE_MAX`). This keeps real flat-scene pills (incl. metadata-only ones the wordmark misses) plus harmless flat false fires, and leaves the damaging textured false fires untouched. Do NOT drop the wordmark arm or loosen the metadata arm back to a bare metadata-OR gate. **Two removal methods, selected by `KnownMark.remove(method=...)` / CLI `--method`:** `reverse-alpha` recovers exact pixels from the captured alpha map (lighter, better on structured backgrounds), `inpaint` erases the mark footprint (`footprint_mask` → MI-GAN, or cv2 without the `migan` extra). `auto` (default) = `resolve_removal_method`: **reverse-alpha for capture marks, inpaint for capture-less** (deterministic, model-independent). Reverse-alpha is the default where a capture exists because it recovers the true pixels and is measured CLEANER than MI-GAN inpaint on the capture marks (doubao/gemini/jimeng — MI-GAN adds a rainbow/smudge artifact on structure) AND lighter (no model/RAM); inpaint is reserved for the capture-less pill. The inpaint backend is chosen by `preferred_inpaint_backend`: **MI-GAN when its extra is installed, else cv2** — big-LaMa is NOT auto-selected (both run on onnxruntime, so availability cannot express intent; big-LaMa stays an explicit `erase --backend lama` opt-in). The inpaint mask MUST be the full NCC-aligned alpha silhouette (`footprint_mask`), NOT the per-image `extract_mask` signature — the signature under-segments and leaves glyph residue (corpus-validated 2026-07). Add a new **reverse-alpha** text mark = one `_text_mark(...)` row + a `TextMarkConfig` with a captured alpha map; do not re-add per-mark `if` branches. A **capture-less inpaint mark** (no alpha map, like `jimeng_pill`) is instead a `has_capture=False` `KnownMark` + a small engine exposing `detect`/`footprint_mask` (geometry + synthetic silhouette) + a metadata/cross-mark gate for its detector -- this path does NOT need a flat capture (removal quality comes from the inpaint backend, so a font-rendered silhouette is data-safe and sufficient). `cli._write_bgr_with_alpha` must NOT zero alpha in the watermark bbox (issue #30 white-box regression). **A new REVERSE-ALPHA visible mark is HARD-BLOCKED on real controlled flat captures** — the alpha map is solved by `scripts/visible_alpha_solve.py` from solid black + gray (+white) captures of the mark produced by the actual app/device at native resolution, committed under `data/<engine>_capture/captures/`. Do NOT synthesize the alpha by font-rendering the wordmark — the user rejected synthetic reconstruction as below the quality bar (2026-06-22), and per-image alpha falloff/variation needs the real capture. Corpus images are NOT a substitute (data-safety: no corpus-derived committed assets; and reverse-alpha needs the flat capture, not real content). So if no flat capture exists for a mark, the work is parked — do not propose synthetic, do not derive from user uploads. (Meta AI, more Samsung locales, and any Grok visible mark are all parked on this; Grok additionally needs confirming it even HAS a visible mark — its known signal is EXIF-only `xai_signature`.)
 - `gemini_engine.py` — visible Gemini-sparkle remover/detector (cv2/numpy, no GPU): top-K size-weighted fusion candidate selection (`_SELECT_TOPK`), corner-promote, over/under-subtraction guards, false-positive gate, self-verify repair. Detection scores the top-K size-weighted matches by full fusion (spatial+gradient+variance) and keeps the highest — NOT the raw-NCC argmax, which re-admits the tiny-patch FPs the size weight suppresses (the osachub 2026-06-12 sub-0.85 corner-sparkle regression; see `docs/module-internals.md`). Keep the 0.85 corner-promote NCC gate; a margin/chroma-gated lower promote was measured and REJECTED 2026-06-11 (~33% FP on non-Google content). Gate any removal candidate on a physical brightness check, not the detector alone.
 - `_text_mark_engine.py` — shared base for the three reverse-alpha text-mark engines (extracted 2026-06-09); the per-engine modules are config-only subclasses. New text mark = a `TextMarkConfig` + a thin subclass + one registry row. Gemini stays a separate engine (different model).
+- `pill_engine.py` — the CAPTURE-LESS Jimeng-basic "AI生成" pill (top-left, issue #54). No alpha map: `detect` is edge-NCC of a synthetic font-rendered silhouette (`assets/jimeng_pill.png`, regenerate via `scripts/render_pill_silhouette.py`; committed, data-safe -- corpus stays out of the repo) in the top-left ROI, calibrated on 61 local real positives to threshold 0.22; `footprint_mask` is a generous FIXED top-left geometry box (NOT the NCC match position -- the synthetic silhouette localizes only approximately, the corner is negative space, so a geometry box removes cleanly while a match box leaves outline residue). `footprint_texture`/`footprint_is_flat` (median-Sobel over that box, `_FLAT_TEXTURE_MAX`) back the metadata-only safe-inpaint gate. Removal is inpaint-only (MI-GAN/cv2). Detector precision is weak (~7% raw false-fire), so it is registry-gated in `remove_auto_marks` via `_keep_pill`: never on Doubao; the bottom-right wordmark removes it unrestricted (~94% precise, survives metadata-STRIPPED uploads); TC260-metadata-only removes it ONLY on a flat footprint (its textured false fires -- ceilings/walls -- are what inpaint smears). Do NOT loosen those gates.
 - `doubao_engine.py` / `jimeng_engine.py` / `samsung_engine.py` — thin `TextMarkEngine` subclasses: Doubao "豆包AI生成" (bottom-right), Jimeng "★ 即梦AI" (bottom-right), Samsung Galaxy AI "✦ Contenuti generati dall'AI" (bottom-LEFT, locale-specific — Italian variant calibrated). Removal = reverse-alpha (always-align) + thin residual inpaint, **with an over-subtraction guard ported from `gemini_engine` (2026-06-20)**: `_reverse_alpha_oversubtracts` predicts the reverse-alpha output PER PIXEL over the glyph body from the INPUT, and when the recovered body lands more than `_OVERSUB_DARK_MARGIN` (25) gray levels below the local ring it abandons the reverse-alpha pixels and inpaints the footprint from the original surroundings (`_inpaint_footprint`) — fixing the dark-pit ghost on dark/mid-tone backgrounds (roadmap P0#8). Predicting per-pixel from the input (not the produced output) keeps a clean full-strength mark byte-identical (no false trip). A detector-only removal test is insufficient — assert visual residual (the textured-shift tests + `tests/test_text_mark_oversubtraction.py`).
-- `region_eraser.py` — universal region eraser (`erase` CLI): cv2 backend default (no deps), optional big-LaMa via onnxruntime (~3.5-4 GB peak RAM, ~5-6 s/call CPU — does not fit a minimal droplet).
+- `region_eraser.py` — universal region eraser (`erase` CLI) and the inpaint backend for the visible-mark fallback. Three backends: `cv2` (default, no deps), `migan` (MI-GAN ONNX, extra `migan`, MIT, ~28 MB / ~0.95 GB peak / ~0.19 s — the droplet-friendly tier, **the preferred backend for the auto inpaint fallback** via `watermark_registry.preferred_inpaint_backend`), `lama` (big-LaMa ONNX, extra `lama`, ~200 MB / ~4.7 GB peak — best quality, does not fit a minimal droplet, explicit opt-in only). **MI-GAN mask polarity is INVERTED** (0=hole/255=known) vs this package's 255-erase convention; `erase_migan` inverts before feeding the model (feeding 255=hole regenerates the whole frame into stripes — corpus-validated). Both ONNX models download on first use, never bundled.
 - `invisible_watermark.py` — decodes the OPEN DWT-DCT watermarks (SD / SDXL / FLUX) via `imwatermark` (extra `detect`, pulls torch). Fragile two ways: (1) does not survive JPEG re-encode/resize; (2) **carrier-fragile on a broad class of pristine images** -- a clean encode->decode round-trip recovers 48/48 on chatgpt/firefly/random but FAILS (28-39/48, below the `_MATCH_48`=44 gate) on the FLUX fox, doubao, a flat FLUX generation, AND a clean synthetic flat fill with no watermark. The failure does NOT track texture; it goes with a degenerate **all-ones decode that is a CARRIER ARTIFACT, not a watermark** (synthetic clean image reproduces it). So `detect_invisible_watermark` is **positive-only**: trust a hit; a `None` is inconclusive unless a same-carrier positive-control embed first recovers >=44. Verified 2026-06-19; full caveat in `docs/watermarking-landscape.md`.
 - `trustmark_detector.py` — Adobe TrustMark open decoder (extra `trustmark`). Do NOT remove the JPEG re-encode false-positive gate — a lone TrustMark hit without it is almost always content noise.
 - `noai/watermark_remover.py` — `WatermarkRemover` with three diffusion pipelines selected by the explicit `pipeline` ctor arg, never inferred from `model_id`: `sdxl` (plain SDXL img2img), `controlnet` (SDXL + canny ControlNet, **the DEFAULT since 2026-06-09**), and `qwen` (Qwen-Image 20B MMDiT img2img, Apache-2.0, CUDA/cloud-class — best **text** preservation (incl. CJK); `_load_qwen_pipeline`/`_run_qwen`, bf16, no MPS fallback; call shape in the pure `_build_qwen_kwargs` using `true_cfg_scale`). Removal comes from the img2img `strength`; ControlNet only preserves text/face STRUCTURE — SynthID CAN survive controlnet on photoreal content at low strength. Qwen CERTIFIED oracle floors (2026-06-20): OpenAI **0.10** (seed-robust, clean on seeds 0-4), Gemini **0.25** (seed 0 verified, pin a seed — Gemini oracle rate-limits volume; higher than the controlnet Gemini floor 0.15). `resolve_strength(..., pipeline="qwen")` carries the Qwen ladder (`_QWEN_VENDOR_STRENGTH`), so `--pipeline qwen` gets the 0.25 Gemini floor automatically (the old manual `--strength 0.25` workaround is retired). `_build_qwen_kwargs` passes an explicit `height`/`width` from the input (floored to /16 via `_qwen_target_size`) — without it the pipeline defaults to a 1024x1024 SQUARE and silently squishes non-square inputs (fixed 2026-06-20). **`qwen` is a MANUAL opt-in only — there is NO auto-router.** Measured (`scripts/fidelity_metrics.py`, OCR-CER / ArcFace / LPIPS / Laplacian-var, NOT eyeball): qwen beats controlnet on ONE niche only — **clean body text on a plain background, no faces** (openai_1/2 CER 0.241 vs 0.385). controlnet wins FACES (it always has) AND **display/decorative text in a scene** (abba poster: controlnet CER 0.114 vs qwen 0.379 — canny holds letter shapes, qwen re-renders and garbles them). So a content `--pipeline auto` router and a faces+text **mixed dual-pass** were prototyped and **DROPPED** (2026-06-20): on the canonical faces+text case controlnet wins every metric incl. text, so mixed loses; and "text→qwen" can't be auto-decided (it is body-vs-display text that matters, undetectable cheaply). qwen stays for callers who KNOW their content is clean-text-heavy and face-free. No face-restore extra ships, by validated decision (every restore approach looked MORE AI-generated). `remove_watermark(region=(x,y,w,h), region_feather=...)` runs the regeneration but feather-composites only the AI box back over the original (via `noai/tiling.feather_region_composite`), preserving the real photo elsewhere — the **AI-enhanced composite** path (`identify` `ai_source_kind == "enhanced"`); the box is supplied by the caller (a C2PA composite manifest carries no reliable machine-readable region, so we do not fabricate one).

docs/module-internals.md

@@ -91,7 +91,7 @@ The cost (mislabel ~8-33% of non-Gemini content as Gemini) outweighs the benefit
 
 **Under-subtraction (the symmetric case, fixed 2026-06-03):** some real Gemini sparkles are rendered MORE opaque than the captured ~0.51, so the fixed-alpha reverse blend UNDER-subtracts and leaves a bright sparkle residual the detector still fires on (measured on the spaces corpus: a visible-removal audit through the registry path left a detectable sparkle on a meaningful fraction of marks, all under-removals, NOT a background-brightness class — failures and successes had the same input confidence and the same background-luma distribution; the discriminator was the removal delta itself). `remove_watermark` now estimates a per-image alpha gain (`_estimate_alpha_gain`: effective sparkle opacity at the bright core vs the local background ring, `a_eff/a_cap`, clamped `[1.0, _ALPHA_GAIN_MAX` 1.94`]`) and scales the alpha to match before the over-sub/blend branch. The gain cleanly separates on the corpus (under-removed marks ~1.47, cleanly-removed ~1.00), and a deadband (`_ALPHA_GAIN_DEADBAND` 1.05) keeps a matching sparkle **byte-identical** to the pre-fix output, so the fix is purely additive (0 regressions on the audit set; the over-sub guard still runs on the scaled alpha as the safety net for an over-shooting estimate). Regression-guarded by `test_gemini_engine.py::TestUnderSubtractionGain` (composites a more-opaque-than-capture sparkle; **asserts on footprint pixels, NOT the detector** — the detector's NCC is degenerate on a flat synthetic background, so a re-detect conf is meaningless there; the real corpus removal drops the detector from ~0.80 to ~0.27).
 
-**False-positive gate (added 2026-06-03):** `detect_watermark`'s shape-only NCC (`spatial*0.5 + gradient*0.3 + var*0.2`) fires on ornate/flat content (text strips, banners, hatching) that coincidentally matches the diamond shape — a real Gemini sparkle is a bright WHITE overlay, so its core sits above the local background, but the NCC is contrast-invariant and cannot see that. The fusion now **demotes** (caps confidence to 0.30) any match that is BOTH low-confidence (`< _SPARKLE_FP_CONF` 0.65) AND has a low core-ring brightness margin (`_core_ring_margin < _SPARKLE_FP_MARGIN` 5). Real sparkles escape via EITHER high confidence (white-bg sparkles score ≥0.79 despite a low margin — the NCC shape match is strong) OR high margin (dark/mid backgrounds, incl. the #36 faint-corner case, lift well clear), so BOTH must fail to demote. The gate is **monotonic** (only ever removes detections, never adds), so it cannot regress the verified-negative corpus (already 0 FPs). On the spaces corpus it demoted 16/495 flagged sparkles (13 carried no AI metadata = content FPs; the 3 AI-meta were visually FPs / a near-invisible white-on-white sparkle whose AI verdict is held by metadata anyway), and dropped the removal-audit failures 20→15 (post-removal flat footprints the NCC re-fired on). `_core_ring_margin` and `_estimate_alpha_gain` share the `_core_and_bg` helper (core 75th-pct brightness vs background-ring median). Regression-guarded by `test_gemini_engine.py::TestSparkleFalsePositiveGate`.
+**False-positive gate (added 2026-06-03):** `detect_watermark`'s shape-only NCC (`spatial*0.5 + gradient*0.3 + var*0.2`) fires on ornate/flat content (text strips, banners, hatching) that coincidentally matches the diamond shape — a real Gemini sparkle is a bright WHITE overlay, so its core sits above the local background, but the NCC is contrast-invariant and cannot see that. The fusion now **demotes** (caps confidence to 0.30) any low-confidence (`< _SPARKLE_FP_CONF` 0.65) match that shows NEITHER real-sparkle signature: a bright core (`_core_ring_margin >= _SPARKLE_FP_MARGIN` 5) OR a crisp star silhouette (`gradient_score >= _SPARKLE_FP_GRAD` 0.55). I.e. demote when `low_margin OR low_grad`. Real sparkles escape via high confidence (white-bg sparkles score ≥0.79 despite a low margin — the NCC shape match is strong), high margin (dark/mid backgrounds, incl. the #36 faint-corner case, lift well clear), OR high gradient (a real sparkle is grad ~0.97–1.0). **The gradient condition (added 2026-06-26) closes the bright-background FP class** the margin check alone missed: a snow+sky photo and a white-background product render both scored ~0.51 at `identify`, because a bright background gives the match a HIGH core-ring margin (it genuinely IS brighter than its surroundings), so the brightness gate read it as a real overlay — but a smooth luminance blob that shape-NCC-matches the rough diamond has low gradient fidelity (the two FPs measured grad 0.105 and 0.463 vs ≥0.8 for real sparkles), so the gradient floor demotes them. The OR is **strictly a superset** of the old margin-only demotion (it only ADDS demotions on bright backgrounds, where a real sparkle keeps grad ~0.97), so it cannot regress a dark/mid sparkle (kept by margin) or a white-bg one (kept by confidence ≥ 0.65). The gate is **monotonic** (only ever removes detections, never adds), so it cannot regress the verified-negative corpus (already 0 FPs); the 2026-06-26 corpus re-sweep flipped only OpenAI/ChatGPT content (no Gemini sparkle exists there) and already-`cleaned/` outputs, all sub-0.5 (below the `identify` threshold), so no provenance verdict changed. On the spaces corpus the original gate demoted 16/495 flagged sparkles (13 carried no AI metadata = content FPs; the 3 AI-meta were visually FPs / a near-invisible white-on-white sparkle whose AI verdict is held by metadata anyway), and dropped the removal-audit failures 20→15 (post-removal flat footprints the NCC re-fired on). `_core_ring_margin` and `_estimate_alpha_gain` share the `_core_and_bg` helper (core 75th-pct brightness vs background-ring median). Regression-guarded by `test_gemini_engine.py::TestSparkleFalsePositiveGate` (incl. `test_bright_background_low_gradient_match_demoted`).
 
 **Self-verify repair (added 2026-06-04):** the gain estimate corrects most under-subtractions, but a tail of strong sparkles still survived reverse-alpha (position jitter, or a gain the `[1.0, 1.94]` clamp could not fully reach). After the reverse blend, `remove_watermark` re-detects via `_verify_and_repair`; when a sparkle at or above `_VERIFY_FALLBACK_CONF` 0.5 (the registry's real fail line) remains, it inpaints the footprint and **keeps that only when it lowers the re-detect confidence** — purely additive (the common clean removal re-detects below 0.5 and is returned untouched, so it can never regress). On the spaces corpus this rescued **4 of the 15 remaining gemini removal-audit failures** (15→11, doubao/jimeng still 0), verified through the registry/CLI path. Costs one extra `detect_watermark` per removal (two when the fallback fires). Regression-guarded by `test_gemini_engine.py::TestVerifyAndRepair` (stubs `detect_watermark` to drive the keep-best control flow, since the NCC is degenerate on flat synthetics).
 
@@ -105,7 +105,7 @@ The 11 survivors are near-white ill-conditioning (reverse-alpha divides by `1-a`
 
 ## `_text_mark_engine.py`
 
-`_text_mark_engine.py` — **shared base for the three reverse-alpha text-mark engines (Doubao/Jimeng/Samsung), extracted 2026-06-09** (they were ~90% byte-identical clones). `TextMarkEngine(config: TextMarkConfig)` owns the whole `locate → extract_mask → detect → _fixed/_aligned_alpha_map → _apply_reverse_alpha → remove_watermark_reverse_alpha` pipeline (+ the asset-keyed `load_alpha_template`/`glyph_silhouette`/`template_match_score` caches). Each engine module is now a thin subclass: it supplies only its `TextMarkConfig` (the tuned constants, the bundled asset, and the bounded structural deltas — `corner` br/bl, `margin_floor` 4/2, `morph_open_size` 5/3, `min_gw` 8/16) plus the test-facing module shims (`_alpha_template`/`_glyph_silhouette`/`_template_match_score` + the constants). Behavior is byte-exact vs the old per-engine code (the three engine test suites pass unchanged). Gemini stays a SEPARATE engine (its multi-size fixed-slot sparkle model is genuinely different). Add a new text mark = a new `TextMarkConfig` + a thin subclass + one registry `_text_mark(...)` row. The engine bullets below describe each mark's calibration history; the LOGIC lives here.
+`_text_mark_engine.py` — **shared base for the three reverse-alpha text-mark engines (Doubao/Jimeng/Samsung), extracted 2026-06-09** (they were ~90% byte-identical clones). `TextMarkEngine(config: TextMarkConfig)` owns the whole `locate → extract_mask → detect → _fixed/_aligned_alpha_map → _apply_reverse_alpha → remove_watermark_reverse_alpha` pipeline (+ the asset-keyed `load_alpha_template`/`glyph_silhouette`/`template_match_score` caches). Each engine module is now a thin subclass: it supplies only its `TextMarkConfig` (the tuned constants, the bundled asset, and the bounded structural deltas — `corner` br/bl, `margin_floor` 4/2, `morph_open_size` 5/3, `min_gw` 8/16) plus the test-facing module shims (`_alpha_template`/`_glyph_silhouette`/`_template_match_score` + the constants). Behavior is byte-exact vs the old per-engine code (the three engine test suites pass unchanged). Gemini stays a SEPARATE engine (its multi-size fixed-slot sparkle model is genuinely different). Add a new text mark = a new `TextMarkConfig` + a thin subclass + one registry `_text_mark(...)` row. The engine bullets below describe each mark's calibration history; the LOGIC lives here. **Small-image detection guard (`_MIN_DETECT_SHORT_SIDE` 200, added 2026-06-26):** `detect` returns not-detected when the image short side is below 200px. Below that the glyph template degrades to the `min_gw` floor (~8px) and `TM_CCOEFF_NORMED` on a few pixels is noise, so an unrelated small geometric shape can spuriously correlate with the CJK silhouette — a 48×48 app-icon chevron scored Doubao 0.41 / Jimeng 0.47 (both above their thresholds), a pure small-size artifact (the same icon upscaled collapses to ~0.06–0.10 NCC at ≥256px). A real AI-generation label is stamped on a full-resolution render (the captured samples are 1086–2048px wide, the smallest positive test image is 1086px), so the floor sits far below any genuine mark while killing the icon/thumbnail band (≤96px); `identify` falls back to "unknown" (the safe default) and removal, gated on detection, is suppressed too. Regression-guarded by `test_{doubao,jimeng,samsung}_engine.py::TestDetect::test_small_image_guarded_from_false_positive`.
 
 **`_apply_reverse_alpha` runs on the glyph crop only:** the blend is a no-op outside the glyph `region` (x, y, w, h) (`(wm - 0)/(1 - 0) == wm`, and a uint8→float32→uint8 round-trip is exact). It copies the frame through and computes the reverse-alpha math on the `region` crop only — byte-identical to the old full-frame pass (verified: Doubao 130 + Jimeng 22 placements, 0 mismatches) but O(glyph) not O(image). The full-frame pass cost ~275 ms on a 12 MP frame for a glyph that is <0.1% of it, once per candidate placement (fixed + aligned ≈ 2×/removal); the crop drops that to ~2 ms. Mirror of the Gemini `_core_and_bg` crop.
 
@@ -165,7 +165,11 @@ Root cause: bad alpha (under-estimated, max ~0.65) + fixed-no-inpaint + tight bo
 
 ## `region_eraser.py`
 
-`region_eraser.py` — universal region eraser (`erase` CLI). `erase(image, boxes=|mask=, backend=)` accepts grayscale (2D) and RGBA (4-channel) inputs on **both** backends (`erase_cv2` and `erase_lama` each split off any alpha plane and re-attach it unchanged, and promote grayscale to BGR for processing — LaMa would otherwise crash on grayscale and drop alpha on BGRA): `boxes_to_mask` → `cv2.inpaint` (`cv2` backend, default, no deps) or big-LaMa via onnxruntime (`lama` backend, extra `lama`, `Carve/LaMa-ONNX` Apache-2.0 model downloaded on first use, never bundled). `erase_lama` crops a padded region around the mask, runs LaMa at its fixed 512² input, pastes only masked pixels back (untouched areas stay pixel-exact). Lazy `_get_lama_session` singleton; `lama_available()` guards the optional import.
+`region_eraser.py` — universal region eraser (`erase` CLI) AND the inpaint backend for the visible-mark fallback (`watermark_registry._inpaint_remove`). `erase(image, boxes=|mask=, backend=)` accepts grayscale (2D) and RGBA (4-channel) inputs on **all** backends (each splits off any alpha plane and re-attaches it unchanged, and promotes grayscale to BGR): `boxes_to_mask` → one of three backends.
+- `cv2` (default, no deps): `cv2.inpaint`.
+- `migan` (extra `migan`, `andraniksargsyan/migan` ONNX, MIT, ~28 MB): `erase_migan`. The MI-GAN ONNX crops around the mask bbox and re-composites internally, so the FULL image is fed at native resolution; only masked pixels are pasted back. **Mask polarity is INVERTED** vs this package's 255-erase convention — the shipped ONNX wants 0=hole / 255=known, so `erase_migan` feeds `(mask<=127)*255`; feeding 255=hole regenerates the whole frame into stripes (corpus-validated 2026-07, cost hours to find). ~0.95 GB peak / ~0.19 s. This is the **preferred** inpaint-fallback backend.
+- `lama` (extra `lama`, `Carve/LaMa-ONNX` Apache-2.0, ~200 MB): `erase_lama` crops a padded region around the mask, runs at LaMa's fixed 512² input, pastes only masked pixels back. Best quality but ~4.7 GB peak — explicit opt-in only, NOT auto-selected.
+Lazy `_get_{lama,migan}_session` singletons; `{lama,migan}_available()` guard the optional imports (both == onnxruntime present). Note both extras install the same onnxruntime, so the two `*_available()` checks are identical — the registry's `preferred_inpaint_backend` therefore prefers MI-GAN whenever onnxruntime is present, and big-LaMa is reachable only by explicit `--backend lama`.
 
 **LaMa-ONNX costs ~3.5-4 GB peak RAM and ~5-6 s/call on CPU** (FFC working set, not arena — `enable_cpu_mem_arena=False` does not help), so it does NOT fit a minimal droplet; the cv2 backend (tens of MB, ~30 ms) does. LaMa quality at low RAM = serverless/GPU, mirroring how raiw.cc offloads SDXL to fal.
 
@@ -239,3 +243,23 @@ KEPT from that work (independently valid for the manual `--pipeline qwen`): the
 `imwrite` returns `False` on an unwritable path (`OSError` caught) instead of raising, matching `cv2.imwrite` semantics. macOS/Linux already accept UTF-8 paths, so it is behavior-neutral there (the bug only reproduces on Windows).
 
 **`to_bgr(image)` (added 2026-06-09)** is the shared channel normalizer: promotes 2D grayscale / (h,w,1) / 4-channel BGRA to 3-channel BGR (a 3-channel input is returned unchanged, no copy). Use it instead of inlining the `cvtColor(GRAY2BGR/BGRA2BGR)` branch — the gemini engine and the `TextMarkEngine` base both route through it so a grayscale/BGRA input (a real Gemini-app export is opaque RGBA) does not crash the `axis=2` channel reductions. cv2/numpy are imported lazily inside the functions, so the module is cheap to import in a bare env.
+
+## CLI commands (`cli.py`)
+
+Full per-command behavior for the skip/exit branches summarized in `CLAUDE.md`'s "How to run". The CLI distinguishes three exit codes: success (0), hard error (1), and a "nothing to do" code (2, `EXIT_NO_VISIBLE_MARK` / `EXIT_NO_INVISIBLE_SIGNAL`) so a wrapping service (raiw.cc) can surface guidance instead of treating an unchanged image as done (the production "it didn't work" / score-0 trap).
+
+### `all`
+
+Full pipeline (visible + invisible + metadata). Same diffusion knobs as `invisible`, plus the visible-pass `--inpaint/--no-inpaint`/`--inpaint-method`. **When the `[gpu]` extra is absent, step 2 (invisible/SynthID) is skipped** — `all` still writes an output (visible mark + metadata stripped) but prints a prominent end-of-run banner ("the invisible (SynthID) watermark was NOT removed") AND exits **non-zero** (1), so a skipped SynthID pass is not mistaken for a clean result (the recurring #14/#47 trap, where the old quiet inline warning was missed). `invisible` already hard-errors without the extra; only `all` continued, hence the loud end-banner. Regression-guarded by `tests/test_cli.py::TestAllCommand::test_all_loud_warning_and_nonzero_exit_when_gpu_missing`. **No-signal skip (P0#5):** step 2 also runs the same `has_invisible_target` gate (see `invisible` below) — when no invisible watermark is detectable and `--force` is not set, step 2 is skipped and the pixels are left intact, but unlike the GPU-missing skip this is a **SUCCESS (exit 0)**: the visible pass + metadata strip still ran and a file is written (the message says so without claiming the image is clean). Distinct exit semantics by design: GPU-missing = couldn't do the work (non-zero); no-signal = nothing to do (zero). Regression-guarded by `test_all_skips_invisible_on_no_signal_but_succeeds`. **Test trap:** any `all` test that exercises the full pipeline MUST `patch("remove_ai_watermarks.invisible_engine.is_available", return_value=True)` — CI installs core+dev only (no `[gpu]`), so an unpatched `all` test takes the skip branch and now hits the non-zero exit. This passed locally (gpu present → `is_available()` True) but red-failed every matrix cell on the v0.11.0 commit (`test_all_basic`/`test_all_visible_step_uses_registry` asserted exit 0); both now patch `is_available` True.
+
+### `invisible`
+
+Diffusion SynthID removal. The `--tile/--no-tile` knob is the *lossless* alternative to a `--max-resolution` downscale for large inputs that OOM on MPS/GPU: it engages only when the long side exceeds `--tile-size` (default 1024); tiles are feather-blended over `--tile-overlap` px (default 128); pair with `--max-resolution 0`. `--adaptive-polish` is a detail-targeted polish that self-gates to a no-op where there is no deficit. `--auto` is deprecated and now a no-op that only warns (the polish it used to enable is ON by default). **No-signal skip (P0#5, roadmap):** before the diffusion runs, the command checks `identify.has_invisible_target(source)` (the `ProvenanceReport.ai_from_metadata` union: C2PA AI-issuer / SynthID proxy, IPTC, AIGC, local gen params, EXIF/xAI, open DWT-DCT / TrustMark — visible marks do NOT count, they are a separate pass). When nothing is locally detectable it does NOT regenerate (that would only degrade a clean image — the dominant paid score-0 cause on no-watermark uploads): it writes NO output, prints guidance that does NOT claim the image is clean (a pixel SynthID is undetectable once its metadata proxy is gone), and exits **`EXIT_NO_INVISIBLE_SIGNAL` (2)** — same value/role as the visible `EXIT_NO_VISIBLE_MARK`. `--force/--no-force` (**default skip = ON**) runs the scrub regardless. The check fails SAFE (a detector exception → run, since leaving a watermark on a paid removal is worse than over-regenerating). Helpers `cli._no_invisible_signal_exit` + `identify.has_invisible_target`; regression-guarded by `tests/test_cli.py::TestInvisibleCommand::{test_invisible_no_signal_skips_and_exits_two,test_invisible_force_runs_scrub_on_no_signal,test_invisible_runs_without_force_when_signal_present}` and `tests/test_identify.py::TestHasInvisibleTargetFailSafe`. **Test trap:** any `invisible`/`all`/`batch` test that exercises the diffusion path on a signal-LESS fixture (e.g. the synthetic `sample_png`) MUST pass `--force`, or the new gate skips step 2 (so `mock_engine.remove_watermark` is never called / `invisible` exits 2).
+
+### `visible`
+
+Known-visible-mark removal. Two methods via `--method` (default `auto`): `reverse-alpha` inverts the captured alpha map to recover exact pixels (CPU, no GPU, better on structured backgrounds); `inpaint` erases the mark footprint (`footprint_mask` → MI-GAN when the `migan` extra is installed, else cv2; see `preferred_inpaint_backend` — big-LaMa is NOT auto-selected). `auto` = `registry.resolve_removal_method`: reverse-alpha for capture marks, inpaint for capture-less (deterministic, model-independent -- reverse-alpha is measured cleaner + lighter than MI-GAN on the capture marks, so inpaint is reserved for the pill); the resolved method is echoed in the status line, and `--method` is shared across `visible`/`all`/`batch`. The inpaint mask is the full NCC-aligned alpha silhouette (`footprint_mask`), NOT the per-image `extract_mask` signature — the signature under-segments and leaves glyph residue (corpus-validated 2026-07; the mask is dilated to absorb alpha-alignment slop). `--mark auto` (default) removes EVERY detected mark in one pass (`registry.remove_auto_marks`, not the single strongest -- a Jimeng-basic image carries both the top-left pill and the bottom-right wordmark) from: the Gemini sparkle, the Doubao "豆包AI生成" text strip, the Jimeng "★ 即梦AI" wordmark, the Samsung Galaxy AI "✦ Contenuti generati dall'AI" strip (bottom-LEFT, Italian locale), and the capture-less Jimeng "AI生成" pill (top-left, `pill_engine`, inpaint-only). The pill's weak edge-NCC detector is gated in `remove_auto_marks` via `_keep_pill` (32k real-upload corpus validation 2026-07): never on Doubao, and two confirmation arms since metadata confirms the platform, not pill presence. (1) The bottom-right wordmark fired — ~94% precise and survives metadata-STRIPPED uploads (screenshots / re-saves) — removes the pill unrestricted. (2) TC260 metadata only (CLI `_aigc_metadata_present` → `pill_metadata`, no wordmark) — ~27% precise, its false fires are textured ceilings/walls that inpaint visibly SMEARS — removes the pill ONLY when the top-left footprint is flat enough for an invisible inpaint (`pill_engine.footprint_is_flat`, median-Sobel ≤ `_FLAT_TEXTURE_MAX`). No confirmation → never removed. `--mark gemini|doubao|jimeng|samsung|jimeng_pill` forces one (choices come from the registry). Under `reverse-alpha`, Gemini/Doubao recover pixels exactly with no inpaint at native; **Jimeng and Samsung add an always-on thin residual inpaint over the glyph footprint** (their marks re-rasterize per image, so reverse-alpha alone leaves a faint outline). For arbitrary logos/objects use `erase`. **When `--mark auto` finds no known mark (the common case — ~74% of real uploads carry no registered visible mark), the command does NOT silently re-serve the input as a finished result.** It runs a cheap metadata-only `identify`, prints actionable guidance (if the image carries an invisible/metadata mark, e.g. an OpenAI/Gemini C2PA image, it points to `all`; otherwise it does NOT imply the image is clean -- it warns that an invisible pixel watermark like SynthID cannot be detected once the metadata proxy is gone and routes to both `all` and `erase --region`), writes NO output file, and exits **`EXIT_NO_VISIBLE_MARK` (2)** — distinct from success (0) and a hard error (1) so a wrapping service (raiw.cc) can surface the message instead of treating the unchanged image as done (the production "it didn't work" / score-0 trap). Same handling for an explicit `--mark <name>` that is not detected. Helper `cli._no_visible_mark_exit`; regression-guarded by `tests/test_cli.py::TestVisibleCommand::test_visible_auto_no_mark_exits_two_with_eraser_hint` and `test_visible_auto_no_mark_routes_to_all_when_metadata`. `--no-detect` still forces the gemini fallback and proceeds (exit 0).
+
+### `batch`
+
+Process every supported image in a directory (output defaults to `<directory>_clean/`, set with `-o`). `--mode visible|invisible|metadata|all` (default `visible`); the invisible/all path reuses the **full `invisible` knob set** (`--strength`/`--steps`/`--guidance-scale`/`--pipeline`/`--controlnet-scale`/`--model`/`--device`/`--max-resolution`/`--min-resolution`/`--upscaler`/`--seed`/`--hf-token`/`--humanize`/`--unsharp`/`--adaptive-polish`/`--tile`/`--tile-size`/`--tile-overlap`/`--force`), plus `--inpaint/--no-inpaint` for the visible pass. `--adaptive-polish` is ON by default; `--auto` is deprecated and a no-op that only warns. **No-signal skip (P0#5):** in invisible/all mode each image runs the same `has_invisible_target` gate — a signal-less image is skipped (no diffusion); in `invisible` mode the input is copied through to the output dir so it stays complete, in `all` mode the visible-removed result is kept and metadata is still stripped. `--force` scrubs every image regardless. One engine cached per pipeline; the polish is resolved once before the loop.

docs/release-and-distribution.md

@@ -23,3 +23,12 @@ stays in `CLAUDE.md`; read this before cutting a release.
 **A failed PyPI upload of one artifact still leaves the other live and you cannot re-upload the same version** — fix the build and cut the next patch.
 
 **Build backend is unpinned `hatchling`** (`[build-system] requires`) since 2026-06-09. History: it was pinned `<1.31` because hatchling 1.30.0 made Metadata-Version 2.5 (PEP 794) the default and the twine bundled in `pypa/gh-action-pypi-publish@release/v1` rejected it (`"'2.5' is not a valid Metadata-Version"`), which **failed the v0.8.3 PyPI upload on 2026-06-01**; hatchling 1.30.1 reverted the default to 2.4. After the workflow moved to `uv publish` (whose uploader accepts 2.5) the pin was belt-and-suspenders only, and once v0.9.0 + v0.10.0 both published wheel+sdist through that path (verified on PyPI) it was dropped. If a future hatchling flips the default to 2.5 again and some consumer chokes, re-pin with a dated comment.
+
+## Dependency CVE-resolution history (`uv-secure`)
+
+The standing `uv-secure` gate in `maintain.sh` is clean; this is the changelog of how each alert was resolved, so a future alert is not re-triaged from scratch.
+
+- **idna** bumped 3.11 -> 3.16, fixing GHSA-65pc-fj4g-8rjx.
+- **aiohttp** bumped 3.13.5 -> 3.14.0 via `uv lock --upgrade-package aiohttp`, fixing GHSA-hg6j-4rv6-33pg + GHSA-jg22-mg44-37j8.
+- **basicsr** Dependabot alert GHSA-86w8-vhw6-q9qq is resolved by removal: the experimental `restore` extra was retired and basicsr is no longer anywhere in the dependency tree.
+- **torch** Dependabot alert **GHSA-rrmf-rvhw-rf47** (`torch.jit.script` memory corruption, vulnerable `<= 2.12.0`) is **dismissed as `not_used`** (2026-06-10): torch is a transitive dep of the optional `gpu` extra only, the codebase never calls `torch.jit` (grep-verified), and **no patched torch version exists** (`first_patched_version` is null), so it cannot be closed by an upgrade — do not re-triage it.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "remove-ai-watermarks"
-version = "0.12.1"
+version = "0.13.0"
 description = "AI watermark remover: strip visible and invisible AI watermarks (Gemini / Nano Banana sparkle, SynthID) and provenance metadata (C2PA, EXIF) from images"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -112,6 +112,15 @@ lama = [
     "onnxruntime>=1.16.0",
     "huggingface-hub>=0.20.0",
 ]
+# Lightweight inpaint backend -- MI-GAN via onnxruntime (andraniksargsyan/migan,
+# MIT). CPU, no torch. Model (~28 MB) downloaded on first use and cached by
+# huggingface_hub; never bundled. ~700-950 MB peak RAM / ~0.19 s/call -- the
+# droplet-friendly tier (vs big-LaMa's ~4.7 GB) and the preferred inpaint backend
+# for the visible-mark fallback when installed. Same runtime as `lama`.
+migan = [
+    "onnxruntime>=1.16.0",
+    "huggingface-hub>=0.20.0",
+]
 # Optional pre-diffusion super-resolution for small inputs (Real-ESRGAN). Loaded via
 # spandrel (MIT) -- a pure model-loader with NO basicsr dependency (it pulls only
 # torch / torchvision / safetensors / numpy / einops).
@@ -136,7 +145,7 @@ dev = [
     "uv-outdated>=0.1.0; python_version >= '3.12'",
     "uv-secure>=0.12.0; python_version >= '3.12'",
 ]
-all = ["remove-ai-watermarks[gpu,detect,trustmark,lama,dev]"]
+all = ["remove-ai-watermarks[gpu,detect,trustmark,lama,migan,dev]"]
 
 # PyTorch Intel-GPU (XPU) wheel index. ``explicit = true`` keeps it inert for
 # the default CPU/CUDA install: uv consults it only when a torch install
@@ -185,6 +194,7 @@ ignore = [
 ]
 
 [tool.ruff.lint.per-file-ignores]
+"scripts/*.py" = ["G004", "S108", "S310", "T20"]
 "tests/*.py" = ["ANN", "S101", "S105", "S106", "S108"]
 "src/remove_ai_watermarks/noai/watermark_remover.py" = ["S603", "S606", "S607", "T201"]  # subprocess calls for auto-install/CUDA fix
 "src/remove_ai_watermarks/noai/c2pa.py" = ["S110"]  # try-except-pass for corrupt file handling

scripts/render_pill_silhouette.py

@@ -0,0 +1,60 @@
+"""Render the SYNTHETIC 'AI生成' pill silhouette asset (data-safe, font-rendered).
+
+The Jimeng-basic TC260 visible label is a rounded pill with 'AI生成' in the TOP-LEFT
+corner (issue #54). Unlike the reverse-alpha marks, this is a CAPTURE-LESS mark:
+the committed asset is a font-rendered binary SILHOUETTE (mark shape only, zero photo
+content), used ONLY to (a) detect the pill by edge-NCC in the top-left corner and
+(b) build the inpaint mask. It is NOT an alpha map -- removal quality comes from the
+inpaint backend (MI-GAN/cv2), so the silhouette need not be pixel-accurate, and the
+synthetic render keeps corpus/user content out of the tracked repo (data-safety).
+
+Detection was calibrated on the retained local corpus (61 real positives + jimeng
+negatives): edge-NCC threshold ~0.22 in the top-left ROI. Re-run to regenerate the
+asset:  uv run python scripts/render_pill_silhouette.py
+
+Requires a CJK font (macOS STHeiti by default); the asset itself is committed, so this
+script only runs when regenerating it (never in CI).
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+_ASSET = Path(__file__).resolve().parents[1] / "src" / "remove_ai_watermarks" / "assets" / "jimeng_pill.png"
+_FONT = "/System/Library/Fonts/STHeiti Medium.ttc"
+
+
+def render_silhouette(w: int = 320) -> np.ndarray:
+    """Rounded-pill outline + 'AI生成' text as a binary silhouette (255 = mark)."""
+    h = int(w * 0.5)
+    im = Image.new("L", (w, h), 0)
+    d = ImageDraw.Draw(im)
+    pad = int(w * 0.03)
+    r = (h - 2 * pad) // 3
+    d.rounded_rectangle([pad, pad, w - pad, h - pad], radius=r, outline=255, width=max(2, w // 90))
+    fsz = int(h * 0.42)
+    font = ImageFont.truetype(_FONT, fsz)
+    txt = "AI生成"
+    tb = d.textbbox((0, 0), txt, font=font)
+    tw, th = tb[2] - tb[0], tb[3] - tb[1]
+    d.text(((w - tw) // 2 - tb[0], (h - th) // 2 - tb[1]), txt, font=font, fill=255)
+    return np.array(im)
+
+
+def main() -> None:
+    try:
+        sil = render_silhouette()
+    except OSError as e:
+        print(f"Font not found ({e}); install a CJK font or edit _FONT.", file=sys.stderr)
+        raise SystemExit(1) from e
+    _ASSET.parent.mkdir(parents=True, exist_ok=True)
+    Image.fromarray(sil).save(_ASSET)
+    print(f"wrote {_ASSET}  ({sil.shape[1]}x{sil.shape[0]})")
+
+
+if __name__ == "__main__":
+    main()

src/remove_ai_watermarks/__init__.py

@@ -11,4 +11,4 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
 
 
-__version__ = "0.12.1"
+__version__ = "0.13.0"

src/remove_ai_watermarks/_text_mark_engine.py

@@ -59,6 +59,19 @@ _OVERSUB_BODY_ALPHA_FLOOR = 0.15  # alpha above which a block pixel counts as gl
 _OVERSUB_INPAINT_DILATE = 9
 _OVERSUB_INPAINT_RADIUS = 4
 
+# Minimum image short side (px) for text-mark DETECTION. Below this the glyph
+# template degrades to the ``min_gw`` floor (~8 px) and TM_CCOEFF_NORMED on a few
+# pixels is noise, so an unrelated small geometric shape can spuriously correlate
+# with the CJK silhouette (2026-06-26 FP: a 48x48 app icon -- a blue chevron --
+# scored Doubao 0.41 / Jimeng 0.47, both above their thresholds). The FP is purely
+# a small-size artifact: the same icon upscaled collapses to ~0.06-0.10 NCC at 256
+# px and above. A real AI-generation text label is stamped on a full-resolution
+# render (the captured samples are 1086-2048 px wide), so 200 px sits far below any
+# genuine mark while killing the icon/thumbnail noise band (<=96 px). Detection is
+# skipped (verdict stays "unknown", the safe default) rather than risk a false
+# positive; removal is gated on detection, so it is suppressed too.
+_MIN_DETECT_SHORT_SIDE = 200
+
 
 @dataclass(frozen=True)
 class TextMarkConfig:
@@ -256,6 +269,17 @@ class TextMarkEngine:
         det = TextMarkDetection()
         if image is None or image.size == 0:
             return det
+        # Guard against the small-image NCC-noise false positive (see
+        # _MIN_DETECT_SHORT_SIDE): an icon/thumbnail is too small to carry a real
+        # text label, and the degraded few-pixel template spuriously correlates.
+        if min(image.shape[:2]) < _MIN_DETECT_SHORT_SIDE:
+            logger.debug(
+                "%s detect: image short side %d < %d; too small to carry the mark, skipping.",
+                c.name,
+                min(image.shape[:2]),
+                _MIN_DETECT_SHORT_SIDE,
+            )
+            return det
         loc = self.locate(image)
         box = self.extract_mask(image, loc)  # box-sized mask (== old full-frame cropped to bbox)
         _x, _y, bw, bh = loc.bbox
@@ -480,3 +504,44 @@ class TextMarkEngine:
             rm = cv2.dilate(rm, kernel)
             best_out = cv2.inpaint(best_out, rm, c.residual_inpaint_radius, cv2.INPAINT_NS)
         return best_out
+
+    # ── Inpaint footprint (for the inpaint-fallback removal path) ────────
+
+    def footprint_mask(
+        self, image: NDArray[Any], *, force: bool = False, dilate: int | None = None
+    ) -> NDArray[Any] | None:
+        """Full-frame uint8 mask (255 = mark) of the mark footprint, for the
+        inpaint-fallback removal path (LaMa / cv2), or None if no placement fits.
+
+        ``force`` is accepted for a uniform engine signature (the caller passes it to
+        every engine) but ignored here -- the text-mark footprint is always the
+        geometry-placed captured silhouette, present with or without a detection.
+
+        Uses the NCC-ALIGNED captured silhouette, NOT the per-image
+        :meth:`extract_mask` signature: the signature under-segments the glyphs, so
+        inpainting it leaves a residual ghost (corpus-validated 2026-07 -- Doubao
+        left a "三包" remnant). The mask is dilated to absorb alpha-alignment slop
+        (a scale/position mismatch at low detect confidence otherwise leaves a thin
+        residual ring); ``dilate`` defaults to a mark-relative margin.
+
+        The caller gates on detection -- this returns the geometric footprint
+        regardless, so a clean corner would be masked too.
+        """
+        image = image_io.to_bgr(image)
+        h, w = image.shape[:2]
+        if h < 32 or w < 64:
+            return None
+        placed = self._aligned_alpha_map(image) or self._fixed_alpha_map(image)
+        if placed is None:
+            return None
+        block, (ax, ay, gw, gh) = placed
+        sil = (block > self.config.residual_alpha_floor).astype(np.uint8) * 255
+        if int((sil > 0).sum()) == 0:
+            return None
+        mask = np.zeros((h, w), np.uint8)
+        ch, cw = min(gh, h - ay), min(gw, w - ax)
+        mask[ay : ay + ch, ax : ax + cw] = sil[:ch, :cw]
+        d = dilate if dilate is not None else max(9, int(0.05 * gw))
+        if d > 0:
+            mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * d + 1, 2 * d + 1)))
+        return mask

src/remove_ai_watermarks/cli.py

@@ -293,6 +293,18 @@ _force_option = click.option(
 )
 
 
+_visible_method_option = click.option(
+    "--method",
+    "removal_method",
+    type=click.Choice(["auto", "reverse-alpha", "inpaint"]),
+    default="auto",
+    help="Visible-mark removal method. auto: reverse-alpha for capture marks (exact "
+    "pixels, lighter), inpaint for the capture-less pill. reverse-alpha recovers "
+    "pixels from a captured alpha map; inpaint erases the footprint (MI-GAN with the "
+    "'migan' extra, else cv2).",
+)
+
+
 def _resolve_auto_polish(auto: bool, adaptive_polish: bool) -> bool:
     """Warn on the retired ``--auto`` flag, returning ``adaptive_polish`` unchanged.
 
@@ -327,9 +339,25 @@ def _warn_if_esrgan_unavailable(upscaler: str) -> None:
         console.print("  Note: --upscaler esrgan needs the 'esrgan' extra; falling back to Lanczos.")
 
 
+def _aigc_metadata_present(path: Path) -> bool:
+    """True when the file carries a China-AIGC (TC260) metadata label. Feeds the
+    weak-detector 'AI生成' pill gate (``remove_auto_marks`` → ``_keep_pill``): metadata
+    confirms Jimeng-class provenance but not pill presence, so the metadata-only arm
+    removes the pill ONLY on a flat, safe-to-inpaint footprint. The reliable
+    bottom-right wordmark is the other, unrestricted confirmation arm (and it survives
+    a metadata-STRIPPED upload)."""
+    with contextlib.suppress(Exception):
+        from remove_ai_watermarks import metadata
+
+        return bool(metadata.aigc_label(path))
+    return False
+
+
 def _remove_visible_auto(
     image: NDArray[Any],
     *,
+    source_path: Path | None = None,
+    removal_method: str = "auto",
     inpaint: bool = True,
     inpaint_method: str = "ns",
     inpaint_strength: float = 0.85,
@@ -340,19 +368,26 @@ def _remove_visible_auto(
     standalone ``visible`` command uses, so EVERY registered mark is handled (the
     Gemini sparkle AND the Doubao/Jimeng/Samsung text marks), not just the sparkle.
     Returns ``(result, label-or-None)``; when no ``in_auto`` mark fires the image is
-    returned unchanged with ``None``. ``inpaint*`` tune the Gemini edge-residual
-    cleanup only (the text engines ignore them).
+    returned unchanged with ``None``. ``removal_method`` selects reverse-alpha vs the
+    inpaint fallback (see ``KnownMark.remove``); ``inpaint*`` tune the Gemini
+    edge-residual cleanup only (the text engines ignore them).
     """
     from remove_ai_watermarks import watermark_registry
 
-    best = watermark_registry.best_auto_mark(image)
-    if best is None:
-        return image, None
+    rmethod: watermark_registry.RemovalMethod = removal_method  # type: ignore[assignment]
     method: Literal["telea", "ns"] = "ns" if inpaint_method == "ns" else "telea"
-    result, _ = watermark_registry.get_mark(best.key).remove(
-        image, inpaint_method=method, inpaint=inpaint, inpaint_strength=inpaint_strength, force=False
+    pill_md = _aigc_metadata_present(source_path) if source_path is not None else False
+    result, removed = watermark_registry.remove_auto_marks(
+        image,
+        pill_metadata=pill_md,
+        method=rmethod,
+        inpaint_method=method,
+        inpaint=inpaint,
+        inpaint_strength=inpaint_strength,
     )
-    return result, best.label
+    if not removed:
+        return image, None
+    return result, ", ".join(removed)
 
 
 # Exit code for the standalone ``visible`` command when no visible mark was
@@ -535,8 +570,9 @@ def main(ctx: click.Context, verbose: bool) -> None:
     type=click.Choice(["auto", *watermark_registry.mark_keys()]),
     default="auto",
     help="Which known visible mark to target (auto picks the strongest detected). "
-    "All marks are removed by exact reverse-alpha against a captured alpha map.",
+    "Removal method is chosen by --method (default auto).",
 )
+@_visible_method_option
 @click.option("--strip-metadata/--keep-metadata", default=True, help="Strip AI metadata from output.")
 @click.pass_context
 def cmd_visible(
@@ -548,14 +584,17 @@ def cmd_visible(
     inpaint_strength: float,
     detect: bool,
     mark: str,
+    removal_method: str,
     strip_metadata: bool,
 ) -> None:
     """Remove a known visible AI watermark from an image.
 
     Finds a known mark in its usual place (Gemini sparkle / Doubao text) via the
-    watermark registry and removes it by exact reverse-alpha against a captured
-    alpha map -- recovering the true pixels, not an inpaint guess. ``--mark auto``
-    picks the strongest detected mark. For arbitrary logos/objects, use ``erase``.
+    watermark registry and removes it. Default ``--method auto`` recovers the true
+    pixels by exact reverse-alpha for the capture marks, and inpaints only the
+    capture-less "AI生成" pill (MI-GAN with the ``migan`` extra, else cv2).
+    ``--mark auto`` picks the strongest detected mark. For arbitrary logos/objects,
+    use ``erase``.
     """
     from remove_ai_watermarks import watermark_registry as registry
 
@@ -574,41 +613,52 @@ def cmd_visible(
     h, w = image.shape[:2]
     console.print(f"  Input:  {source.name}  ({w}x{h})")
 
-    # Resolve the target mark from the known-watermark registry. ``auto`` scans
-    # every in-auto mark in its usual place and picks the strongest; an explicit
-    # ``--mark <key>`` targets that one (the user asserts its presence).
-    if mark == "auto":
-        best = registry.best_auto_mark(image)
-        if best is None:
-            console.print("  No known visible mark detected (gemini / doubao / jimeng / samsung).")
-            if detect:
-                _no_visible_mark_exit(source)
-            target = "gemini"  # forced (no-detect): fall back to the default mark
-        else:
-            target = best.key
-            console.print(f"  Mark auto: {best.label}  ({best.location}, conf {best.confidence:.2f})")
-    else:
-        target = mark
-
-    chosen = registry.get_mark(target)
-    det = chosen.detect(image)
-    if detect and not det.detected:
-        console.print(f"  {chosen.label} not detected  (conf {det.confidence:.2f}). Use --no-detect to force.")
-        _no_visible_mark_exit(source)
-    if det.detected:
-        console.print(f"  {chosen.label} detected  ({chosen.location}, conf {det.confidence:.2f})")
-
     method: Literal["telea", "ns"] = "ns" if inpaint_method == "ns" else "telea"
-    t0 = time.monotonic()
-    with console.status(f"Removing {chosen.label}... ({chosen.recovery})"):
-        result, _ = chosen.remove(
-            image,
-            inpaint_method=method,
-            inpaint=inpaint,
-            inpaint_strength=inpaint_strength,
-            force=not detect,
-        )
-    elapsed = time.monotonic() - t0
+
+    # ``auto`` removes EVERY detected in_auto mark in one pass (a Jimeng-basic image
+    # carries the top-left pill AND the bottom-right wordmark); an explicit
+    # ``--mark <key>`` targets that one (the user asserts its presence).
+    if mark == "auto" and detect:
+        t0 = time.monotonic()
+        with console.status("Detecting & removing visible marks..."):
+            result, removed = registry.remove_auto_marks(
+                image,
+                pill_metadata=_aigc_metadata_present(source),
+                method=removal_method,  # type: ignore[arg-type]
+                inpaint_method=method,
+                inpaint=inpaint,
+                inpaint_strength=inpaint_strength,
+            )
+        elapsed = time.monotonic() - t0
+        if not removed:
+            console.print("  No known visible mark detected (gemini / doubao / jimeng / jimeng-pill / samsung).")
+            _no_visible_mark_exit(source)
+        console.print(f"  Removed: {', '.join(removed)}")
+    else:
+        target = "gemini" if mark == "auto" else mark  # --no-detect auto: gemini fallback
+        chosen = registry.get_mark(target)
+        det = chosen.detect(image)
+        if detect and not det.detected:
+            console.print(f"  {chosen.label} not detected  (conf {det.confidence:.2f}). Use --no-detect to force.")
+            _no_visible_mark_exit(source)
+        if det.detected:
+            console.print(f"  {chosen.label} detected  ({chosen.location}, conf {det.confidence:.2f})")
+        resolved = registry.resolve_removal_method(removal_method, chosen.has_capture)  # type: ignore[arg-type]
+        if resolved == "inpaint" and not registry.inpaint_model_available():
+            console.print(
+                "  Note: --method inpaint using cv2 (install the 'migan' extra for a lightweight ONNX model)."
+            )
+        t0 = time.monotonic()
+        with console.status(f"Removing {chosen.label}... ({resolved})"):
+            result, _ = chosen.remove(
+                image,
+                method=removal_method,  # type: ignore[arg-type]
+                inpaint_method=method,
+                inpaint=inpaint,
+                inpaint_strength=inpaint_strength,
+                force=not detect,
+            )
+        elapsed = time.monotonic() - t0
 
     # Save (rejoins the original alpha plane unchanged)
     output.parent.mkdir(parents=True, exist_ok=True)
@@ -653,9 +703,10 @@ def _parse_region(spec: str) -> tuple[int, int, int, int]:
 )
 @click.option(
     "--backend",
-    type=click.Choice(["cv2", "lama"]),
+    type=click.Choice(["cv2", "migan", "lama"]),
     default="cv2",
-    help="Inpaint backend. cv2: instant, no deps. lama: onnxruntime big-LaMa, better quality (extra 'lama').",
+    help="Inpaint backend. cv2: instant, no deps. migan: light ONNX MI-GAN, ~1 GB RAM, "
+    "near-LaMa quality (extra 'migan'). lama: big-LaMa, best quality but ~4.7 GB RAM (extra 'lama').",
 )
 @click.option("--inpaint-method", type=click.Choice(["telea", "ns"]), default="telea", help="cv2 inpaint method.")
 @click.option("--dilate", type=int, default=3, help="Grow the box by this many px before inpainting.")
@@ -666,7 +717,7 @@ def cmd_erase(
     source: Path,
     regions: tuple[str, ...],
     output: Path | None,
-    backend: Literal["cv2", "lama"],
+    backend: Literal["cv2", "migan", "lama"],
     inpaint_method: str,
     dilate: int,
     strip_metadata: bool,
@@ -999,6 +1050,7 @@ def cmd_identify(ctx: click.Context, source: Path, no_visible: bool, as_json: bo
 @click.option(
     "--inpaint-method", type=click.Choice(["ns", "telea", "gaussian"]), default="ns", help="Inpainting method."
 )
+@_visible_method_option
 @_strength_option
 @click.option("--steps", type=int, default=50, help="Number of denoising steps for invisible removal.")
 @_pipeline_option
@@ -1036,6 +1088,7 @@ def cmd_all(
     output: Path | None,
     inpaint: bool,
     inpaint_method: Literal["ns", "telea", "gaussian"],
+    removal_method: str,
     strength: float | None,
     steps: int,
     pipeline: str,
@@ -1105,7 +1158,9 @@ def cmd_all(
         console.print(f"    Input: {source.name}  ({w}x{h})")
 
         with console.status("Removing visible watermark..."):
-            result, removed_label = _remove_visible_auto(image, inpaint=inpaint, inpaint_method=inpaint_method)
+            result, removed_label = _remove_visible_auto(
+                image, source_path=source, removal_method=removal_method, inpaint=inpaint, inpaint_method=inpaint_method
+            )
             if removed_label is not None:
                 console.print(f"    Visible watermark removed ({removed_label})")
             else:
@@ -1244,6 +1299,7 @@ def _process_batch_image(
     seed: int | None,
     hf_token: str | None,
     humanize: float,
+    removal_method: str = "auto",
     unsharp: float = 0.0,
     max_resolution: int = 0,
     min_resolution: int = 1024,
@@ -1276,7 +1332,7 @@ def _process_batch_image(
         if image is None:
             raise ValueError("Failed to read image")
 
-        result, _ = _remove_visible_auto(image, inpaint=inpaint)
+        result, _ = _remove_visible_auto(image, source_path=img_path, removal_method=removal_method, inpaint=inpaint)
 
         _write_bgr_with_alpha(out_path, result, alpha)
         saved_alpha = alpha
@@ -1361,6 +1417,7 @@ def _process_batch_image(
 @_strength_option
 @click.option("--steps", type=int, default=50, help="Number of denoising steps (invisible mode).")
 @click.option("--inpaint/--no-inpaint", default=True, help="Apply inpainting (visible mode).")
+@_visible_method_option
 @click.option(
     "--humanize", type=float, default=0.0, help="Analog Humanizer film grain intensity (0 = off, typical: 2.0-6.0)."
 )
@@ -1402,6 +1459,7 @@ def cmd_batch(
     seed: int | None,
     hf_token: str | None,
     inpaint: bool,
+    removal_method: str,
     humanize: float,
     unsharp: float,
     max_resolution: int,
@@ -1468,6 +1526,7 @@ def cmd_batch(
                     seed=seed,
                     hf_token=hf_token,
                     humanize=humanize,
+                    removal_method=removal_method,
                     unsharp=unsharp,
                     max_resolution=max_resolution,
                     min_resolution=min_resolution,

src/remove_ai_watermarks/gemini_engine.py

@@ -185,6 +185,21 @@ class GeminiEngine:
     # fail to demote.
     _SPARKLE_FP_CONF = 0.65
     _SPARKLE_FP_MARGIN = 5.0
+    # Bright-background content false positives (2026-06-26 landing-page FPs: a snow+sky
+    # photo and a white-background product render both scored ~0.51). The margin gate
+    # above cannot catch them -- a bright background gives the "core" a HIGH core-ring
+    # margin (it is genuinely brighter than its surroundings), so the brightness check
+    # reads it as a real overlay. The discriminating signature is the GRADIENT NCC: a
+    # real white sparkle is a crisp star silhouette (grad ~0.97-1.0 on the synthetic
+    # composites, ~0.96 on the real #36 corner sparkle), while a smooth luminance blob
+    # that shape-NCC-matches the rough outline has low gradient fidelity (the two FPs
+    # measured 0.105 and 0.463). So ALSO demote a low-confidence match whose gradient
+    # NCC is below this floor, regardless of margin -- 0.55 sits well above the worst FP
+    # (0.463) and far below every real sparkle (>=0.8). This only ADDS demotions on
+    # bright backgrounds (a real bright-bg sparkle keeps grad ~0.97), so it cannot
+    # regress a dark/mid sparkle (already kept by margin) or a white-bg one (kept by
+    # confidence >= 0.65, above the gate).
+    _SPARKLE_FP_GRAD = 0.55
 
     # Self-verify fallback. The gain estimate corrects most under-subtractions, but
     # on the spaces corpus a tail of strong sparkles still survived reverse-alpha
@@ -399,16 +414,24 @@ class GeminiEngine:
         # best_fused is the selected candidate's spatial*0.5 + grad*0.3 + var*0.2.
         confidence = best_fused
 
-        # False-positive gate: a low-confidence shape match whose core is NOT brighter
-        # than its surroundings is a content false positive, not a white sparkle overlay.
+        # False-positive gate: a low-confidence match that shows NEITHER real-sparkle
+        # signature is a content false positive, not a white sparkle overlay. A real
+        # sparkle proves itself by a bright core (high core-ring margin, on dark/mid
+        # backgrounds) OR a crisp star silhouette (high gradient NCC, on any background
+        # incl. bright). Demote when both are weak -- this catches the dark/mid no-core
+        # FP (low margin) AND the bright-background smooth-blob FP (high margin but low
+        # gradient), which the margin check alone misses. See _SPARKLE_FP_GRAD.
         if confidence < self._SPARKLE_FP_CONF:
             margin = self._core_ring_margin(image, self.get_interpolated_alpha(best_scale), (pos_x, pos_y))
-            if margin is not None and margin < self._SPARKLE_FP_MARGIN:
+            low_margin = margin is not None and margin < self._SPARKLE_FP_MARGIN
+            low_grad = grad_score < self._SPARKLE_FP_GRAD
+            if low_margin or low_grad:
                 logger.debug(
-                    "Sparkle FP gate: conf=%.3f, core-ring margin=%.1f < %.1f; demoting.",
+                    "Sparkle FP gate: conf=%.3f, core-ring margin=%s, grad=%.3f < %.2f; demoting.",
                     confidence,
-                    margin,
-                    self._SPARKLE_FP_MARGIN,
+                    f"{margin:.1f}" if margin is not None else "n/a",
+                    grad_score,
+                    self._SPARKLE_FP_GRAD,
                 )
                 confidence = min(confidence, 0.30)
 
@@ -580,6 +603,40 @@ class GeminiEngine:
             self._reverse_alpha_blend(result, alpha_map, pos)
         return self._verify_and_repair(result, alpha_map, pos, size)
 
+    def footprint_mask(self, image: NDArray[Any], *, force: bool = False, dilate: int = 13) -> NDArray[Any] | None:
+        """Full-frame uint8 mask (255 = sparkle) of the sparkle footprint, for the
+        inpaint-fallback removal path (LaMa / cv2), or None.
+
+        The footprint is the interpolated captured alpha at the detected scale --
+        the same region reverse-alpha operates on. When ``force`` and nothing is
+        detected, falls back to the default sparkle slot for the image size (the
+        ``--no-detect`` path). The caller gates on the trust-confidence detection.
+        """
+        image = image_io.to_bgr(image)
+        h, w = image.shape[:2]
+        det = self.detect_watermark(image)
+        if det.detected:
+            x, y, scale = det.region[0], det.region[1], det.region[2]
+        elif force:
+            cfg = get_watermark_config(w, h)
+            x, y = cfg.get_position(w, h)
+            scale = cfg.logo_size
+        else:
+            return None
+        alpha = self.get_interpolated_alpha(scale)
+        fp = self._footprint_indices(alpha, (x, y), image.shape)
+        if fp is None:
+            return None
+        aroi, (y1, y2, x1, x2) = fp
+        sil = (aroi > 0.10).astype(np.uint8) * 255
+        if int((sil > 0).sum()) == 0:
+            return None
+        mask = np.zeros((h, w), np.uint8)
+        mask[y1:y2, x1:x2] = sil
+        if dilate > 0:
+            mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * dilate + 1, 2 * dilate + 1)))
+        return mask
+
     def remove_watermark_custom(
         self,
         image: NDArray[Any],

src/remove_ai_watermarks/pill_engine.py

@@ -0,0 +1,171 @@
+"""Jimeng-basic 'AI生成' pill: a CAPTURE-LESS visible mark (issue #54).
+
+The Jimeng free-tier TC260 label is a rounded pill with 'AI生成' in the TOP-LEFT
+corner -- distinct from the reverse-alpha ``jimeng`` "★ 即梦AI" mark (bottom-right).
+No flat capture / alpha map exists for it, so it is removed by INPAINT, not
+reverse-alpha:
+
+  * Detect: edge-NCC of a font-rendered SILHOUETTE (``assets/jimeng_pill.png``,
+    synthetic, data-safe -- see ``scripts/render_pill_silhouette.py``) against the
+    top-left ROI, at the pill's known width fraction. Corpus-calibrated threshold
+    (61 real positives + jimeng negatives): ``_DETECT_THRESHOLD`` 0.22.
+  * Remove: place the pill footprint at the matched location and inpaint it
+    (MI-GAN / cv2 via the registry). Quality comes from the inpaint backend, so the
+    silhouette need not be pixel-accurate -- which is why a synthetic render is
+    sufficient and no corpus-derived asset is committed.
+
+Geometry measured on 51 real examples (8 resolutions, all 3:4): width ~0.161*W,
+height ~0.091*W, top-left, margins ~0.02-0.05.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, NamedTuple
+
+import cv2
+import numpy as np
+
+from remove_ai_watermarks import image_io
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+# cv2/numpy boundary: cv2 ships no usable type info, so strict pyright cannot know
+# its array element types. Relax the unknown-type rules for this file only; the
+# public signatures are still annotated with NDArray[Any].
+# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportMissingTypeStubs=false, reportMissingImports=false, reportArgumentType=false, reportAssignmentType=false, reportReturnType=false, reportCallIssue=false, reportIndexIssue=false, reportOperatorIssue=false, reportOptionalMemberAccess=false, reportOptionalSubscript=false, reportAttributeAccessIssue=false, reportUnnecessaryComparison=false
+
+_ASSET = Path(__file__).parent / "assets" / "jimeng_pill.png"
+
+# Geometry (fractions of image WIDTH unless noted); top-left corner.
+_WIDTH_FRAC = 0.161
+_ROI_W_FRAC = 0.34  # search window width (of W)
+_ROI_H_FRAC = 0.14  # search window height (of H)
+_DETECT_THRESHOLD = 0.22  # edge-NCC gate, corpus-calibrated
+# Inpaint mask GEOMETRY (fractions of W unless noted): a generous fixed top-left box
+# covering the pill (measured ~0.167*W wide, ~0.09*W tall, margin ~0.02-0.05) plus
+# margin. The mask uses stable geometry, NOT the NCC match position -- the synthetic
+# silhouette localizes only approximately, and the corner is negative space, so
+# over-covering is harmless while a match-positioned box leaves outline residue.
+_MASK_X0, _MASK_Y0 = 0.012, 0.006  # x0 of W, y0 of H
+_MASK_W, _MASK_H = 0.205, 0.115  # width of W, height of W
+
+# Background-flatness gate for the metadata-only pill arm (see remove_auto_marks).
+# The pill detector is weak (~7% raw false-fire); metadata confirms the platform,
+# not pill presence, so its false fires are real Jimeng-class content WITHOUT a pill.
+# Those false fires cluster on TEXTURED top-left corners (ceiling fixtures, structure)
+# where inpaint visibly SMEARS, while real pills and harmless false fires sit on FLAT
+# corners (sky / wall / solid) where inpaint is invisible. So the metadata-only arm
+# removes the pill only when the footprint background is flat enough for a safe,
+# invisible inpaint. Threshold = median Sobel magnitude over the footprint box at a
+# normalized width; corpus-validated on 32k real uploads 2026-07 (real pills median
+# ~3.2, textured-ceiling false fires median ~8+). The reliable bottom-right wordmark
+# arm is NOT texture-gated -- a wordmark-confirmed pill is removed regardless.
+_FLAT_TEXTURE_MAX = 6.0
+
+_silhouette: NDArray[Any] | None = None
+
+
+class PillDetection(NamedTuple):
+    detected: bool
+    confidence: float
+    region: tuple[int, int, int, int]  # x, y, w, h of the matched pill
+
+
+def _load_silhouette() -> NDArray[Any] | None:
+    global _silhouette
+    if _silhouette is None:
+        if not _ASSET.exists():
+            return None
+        _silhouette = image_io.imread(str(_ASSET), cv2.IMREAD_GRAYSCALE)
+    return _silhouette
+
+
+def _grad(gray: NDArray[Any]) -> NDArray[Any]:
+    gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3)
+    gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
+    return cv2.normalize(cv2.magnitude(gx, gy), None, 0, 255, cv2.NORM_MINMAX)
+
+
+class PillEngine:
+    """Detect + inpaint-mask the top-left 'AI生成' pill (edge-NCC, no reverse-alpha)."""
+
+    def _match(self, image: NDArray[Any]) -> tuple[float, tuple[int, int, int, int]] | None:
+        sil = _load_silhouette()
+        if sil is None or image is None or image.size == 0:
+            return None
+        h, w = image.shape[:2]
+        if h < 64 or w < 64:
+            return None
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image.ndim == 3 else image
+        rh, rw = int(h * _ROI_H_FRAC), int(w * _ROI_W_FRAC)
+        roi = gray[0:rh, 0:rw]
+        tw = max(24, int(_WIDTH_FRAC * w))
+        th = max(12, int(tw * sil.shape[0] / sil.shape[1]))
+        if th >= rh or tw >= rw:
+            return None
+        tmpl = cv2.resize(sil, (tw, th))
+        res = cv2.matchTemplate(_grad(roi.astype(np.float32)), _grad(tmpl.astype(np.float32)), cv2.TM_CCOEFF_NORMED)
+        _, score, _, loc = cv2.minMaxLoc(res)
+        return float(score), (int(loc[0]), int(loc[1]), tw, th)
+
+    def detect(self, image: NDArray[Any]) -> PillDetection:
+        m = self._match(image)
+        if m is None:
+            return PillDetection(False, 0.0, (0, 0, 0, 0))
+        score, box = m
+        return PillDetection(score >= _DETECT_THRESHOLD, score, box)
+
+    def _footprint_box(self, image: NDArray[Any]) -> tuple[int, int, int, int] | None:
+        h, w = image.shape[:2]
+        x0, y0 = int(_MASK_X0 * w), int(_MASK_Y0 * h)
+        x1, y1 = min(w, x0 + int(_MASK_W * w)), min(h, y0 + int(_MASK_H * w))
+        if x1 <= x0 or y1 <= y0:
+            return None
+        return x0, y0, x1, y1
+
+    def footprint_texture(self, image: NDArray[Any]) -> float:
+        """Median gradient magnitude over the fixed top-left footprint box at a
+        normalized width. A robust flatness proxy: low = flat (sky / wall / solid,
+        inpaint invisible), high = textured (ceiling fixtures / structure, inpaint
+        smears). Median (not mean) so the pill's own edges -- a minority of the box --
+        do not inflate it. Backs the metadata-only arm's safe-inpaint gate."""
+        if image is None or image.size == 0:
+            return 0.0
+        box = self._footprint_box(image)
+        if box is None:
+            return 0.0
+        x0, y0, x1, y1 = box
+        crop = image[y0:y1, x0:x1]
+        gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY) if crop.ndim == 3 else crop
+        tw = 220
+        gray = cv2.resize(gray, (tw, max(1, int(gray.shape[0] * tw / gray.shape[1])))).astype(np.float32)
+        gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3)
+        gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
+        return float(np.median(cv2.magnitude(gx, gy)))
+
+    def footprint_is_flat(self, image: NDArray[Any], *, thresh: float = _FLAT_TEXTURE_MAX) -> bool:
+        """True when the top-left footprint is flat enough for an invisible inpaint."""
+        return self.footprint_texture(image) <= thresh
+
+    def footprint_mask(self, image: NDArray[Any], *, force: bool = False) -> NDArray[Any] | None:
+        """Full-frame uint8 mask (255 = pill) over the pill's known top-left region.
+
+        Uses stable GEOMETRY (a generous fixed box), not the NCC match position: the
+        synthetic silhouette localizes only approximately, so a match-positioned mask
+        leaves outline residue, while the top-left corner is negative space, so a
+        generous geometric box removes the pill cleanly and harmlessly. The caller
+        gates on :meth:`detect`, so a clean corner is never masked. ``force`` is
+        accepted for a uniform engine signature but ignored (the geometry box is
+        fixed regardless)."""
+        if image is None or image.size == 0:
+            return None
+        box = self._footprint_box(image)
+        if box is None:
+            return None
+        x0, y0, x1, y1 = box
+        h, w = image.shape[:2]
+        mask = np.zeros((h, w), np.uint8)
+        mask[y0:y1, x0:x1] = 255
+        return mask

src/remove_ai_watermarks/region_eraser.py

@@ -9,10 +9,15 @@ deterministic per-generator engines (Gemini sparkle, Doubao) do not cover.
 Backends:
   - ``cv2`` (default): ``cv2.inpaint`` (Telea / Navier-Stokes). Instant, no extra
     dependencies, lower quality on large or textured regions.
+  - ``migan`` (optional, extra ``migan``): MI-GAN via onnxruntime
+    (``andraniksargsyan/migan``, MIT). CPU, ~28 MB model, ~700-950 MB peak RAM,
+    ~0.19 s/call -- the droplet-friendly tier: near-big-LaMa quality on small
+    marks at ~5x less RAM and ~8x faster. Model downloaded on first use.
   - ``lama`` (optional, extra ``lama``): big-LaMa via onnxruntime
-    (``Carve/LaMa-ONNX``, Apache-2.0). CPU, resolution-robust, much better on
-    texture. The model (~200 MB) is downloaded on first use and cached by
-    huggingface_hub; it is never bundled in this repo.
+    (``Carve/LaMa-ONNX``, Apache-2.0). CPU, resolution-robust, best quality on
+    texture but ~200 MB model and ~4.7 GB peak RAM (too heavy for a small host).
+    The model is downloaded on first use and cached by huggingface_hub; it is
+    never bundled in this repo.
 """
 
 # cv2/numpy boundary: cv2 ships no usable type info, so strict pyright cannot know
@@ -32,13 +37,17 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
-Backend = Literal["cv2", "lama"]
+Backend = Literal["cv2", "lama", "migan"]
 
 _LAMA_REPO = "Carve/LaMa-ONNX"
 _LAMA_FILE = "lama_fp32.onnx"
 
-# Cached onnxruntime session (loading is expensive; reuse across calls).
+_MIGAN_REPO = "andraniksargsyan/migan"
+_MIGAN_FILE = "migan.onnx"
+
+# Cached onnxruntime sessions (loading is expensive; reuse across calls).
 _lama_session: object | None = None
+_migan_session: object | None = None
 
 
 def boxes_to_mask(
@@ -167,6 +176,72 @@ def erase_lama(image_bgr: NDArray[Any], mask: NDArray[Any]) -> NDArray[Any]:
     return result
 
 
+def migan_available() -> bool:
+    """True when the optional MI-GAN backend can run (onnxruntime installed)."""
+    from .optional_deps import module_available
+
+    return module_available("onnxruntime")
+
+
+def _get_migan_session() -> object:
+    """Load (once) the MI-GAN ONNX session, downloading the model on first use."""
+    global _migan_session
+    if _migan_session is not None:
+        return _migan_session
+
+    import onnxruntime as ort
+    from huggingface_hub import hf_hub_download
+
+    model_path = hf_hub_download(repo_id=_MIGAN_REPO, filename=_MIGAN_FILE)
+    logger.info("Loading MI-GAN ONNX model: %s", model_path)
+    _migan_session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+    return _migan_session
+
+
+def erase_migan(image_bgr: NDArray[Any], mask: NDArray[Any]) -> NDArray[Any]:
+    """Inpaint ``mask`` (255 = erase) with MI-GAN via onnxruntime (CPU).
+
+    The MI-GAN ONNX pipeline crops around the mask bbox internally and re-composites,
+    so the full image is fed at native resolution. Only the masked pixels are pasted
+    back, so untouched areas stay pixel-exact.
+
+    Mask polarity: the shipped ``andraniksargsyan/migan`` ONNX expects 0 = hole
+    (inpaint) / 255 = known (keep) -- the INVERSE of this package's 255-erase
+    convention -- so the mask is inverted before feeding the model (corpus-validated
+    2026-07; feeding 255=hole regenerates the whole frame into stripes).
+
+    Like ``erase_lama``, accepts 1-channel (grayscale) and 4-channel (BGRA) input.
+    """
+    if image_bgr.ndim == 2:
+        bgr = erase_migan(cv2.cvtColor(image_bgr, cv2.COLOR_GRAY2BGR), mask)
+        return cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
+    if image_bgr.ndim == 3 and image_bgr.shape[2] == 4:
+        bgr = erase_migan(np.ascontiguousarray(image_bgr[:, :, :3]), mask)
+        return np.dstack([bgr, image_bgr[:, :, 3]])
+
+    session = _get_migan_session()
+    inp = session.get_inputs()  # type: ignore[attr-defined]
+    img_name, mask_name = inp[0].name, inp[1].name
+    h, w = image_bgr.shape[:2]
+
+    rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+    img_in = np.transpose(rgb, (2, 0, 1))[None].astype(np.uint8)  # (1,3,H,W)
+    # invert to MI-GAN polarity: 255 where KNOWN (keep), 0 where hole (erase)
+    known = (mask <= 127).astype(np.uint8) * 255
+    mask_in = known[None, None]  # (1,1,H,W)
+
+    out = session.run(None, {img_name: img_in, mask_name: mask_in})[0]  # type: ignore[attr-defined]
+    res = np.transpose(np.asarray(out)[0], (1, 2, 0)).astype(np.uint8)  # (H',W',3) RGB
+    if res.shape[:2] != (h, w):
+        res = cv2.resize(res, (w, h), interpolation=cv2.INTER_LINEAR)
+    out_bgr = cv2.cvtColor(res, cv2.COLOR_RGB2BGR)
+
+    result = image_bgr.copy()
+    hole = mask > 127
+    result[hole] = out_bgr[hole]
+    return result
+
+
 def erase(
     image_bgr: NDArray[Any],
     *,
@@ -191,6 +266,12 @@ def erase(
     if not mask.any():
         return image_bgr.copy()
 
+    if backend == "migan":
+        if not migan_available():
+            raise RuntimeError(
+                "MI-GAN backend requires onnxruntime. Install the extra: pip install 'remove-ai-watermarks[migan]'"
+            )
+        return erase_migan(image_bgr, mask)
     if backend == "lama":
         if not lama_available():
             raise RuntimeError(

src/remove_ai_watermarks/watermark_registry.py

@@ -41,6 +41,11 @@ if TYPE_CHECKING:
 InpaintMethod = Literal["telea", "ns"]
 Region = tuple[int, int, int, int]
 
+# Removal method selection for the visible pass. ``auto`` prefers the inpaint
+# fallback (LaMa footprint) when the ``lama`` extra is installed, else reverse-alpha
+# for marks with a captured alpha map (cv2 inpaint for capture-less marks).
+RemovalMethod = Literal["auto", "reverse-alpha", "inpaint"]
+
 
 @dataclass(frozen=True)
 class MarkDetection:
@@ -62,9 +67,10 @@ class KnownMark:
     label: str
     location: str  # usual place, human-readable ("bottom-right")
     in_auto: bool  # participate in `--mark auto` scanning
-    recovery: str  # removal strategy (all reverse-alpha today)
+    recovery: str  # default removal strategy label ("reverse-alpha")
     _detect: Callable[[NDArray[Any]], MarkDetection]
     _remove: Callable[..., tuple[NDArray[Any], Region | None]]
+    has_capture: bool = True  # a captured alpha map exists (reverse-alpha possible)
 
     def detect(self, image: NDArray[Any]) -> MarkDetection:
         return self._detect(image)
@@ -73,20 +79,29 @@ class KnownMark:
         self,
         image: NDArray[Any],
         *,
+        method: RemovalMethod = "auto",
         inpaint_method: InpaintMethod = "ns",
         inpaint: bool = True,
         inpaint_strength: float = 0.85,
         force: bool = False,
     ) -> tuple[NDArray[Any], Region | None]:
-        """Remove this mark by reverse-alpha; returns ``(result, region)`` where
-        ``region`` is the removed mark's bbox (for residual-inpaint positioning),
-        or None if nothing was removed. NB: the CLI does NOT use ``region`` to
-        clear alpha on save -- that zeroing caused the issue-#30 white box.
+        """Remove this mark; returns ``(result, region)`` where ``region`` is the
+        removed mark's bbox (for residual-inpaint positioning), or None if nothing
+        was removed. NB: the CLI does NOT use ``region`` to clear alpha on save --
+        that zeroing caused the issue-#30 white box.
 
-        ``inpaint`` / ``inpaint_strength`` / ``inpaint_method`` tune the Gemini
-        reverse-alpha edge-residual cleanup only. ``force`` removes at the mark's
-        usual location even without a positive detection (the ``--no-detect`` path).
+        ``method`` selects the removal path: ``reverse-alpha`` recovers the true
+        pixels from the captured alpha map (lighter, exact, better on structured
+        backgrounds); ``inpaint`` erases the footprint with LaMa (or cv2 when the
+        ``lama`` extra is absent), needing no capture; ``auto`` (default) prefers
+        inpaint when LaMa is installed and falls back to reverse-alpha otherwise
+        (cv2 inpaint for capture-less marks). ``inpaint``/``inpaint_strength``/
+        ``inpaint_method`` tune the Gemini reverse-alpha edge-residual cleanup only.
+        ``force`` removes at the mark's usual location even without a positive
+        detection (the ``--no-detect`` path).
         """
+        if resolve_removal_method(method, self.has_capture) == "inpaint":
+            return _inpaint_remove(self, image, force)
         return self._remove(image, inpaint_method, inpaint, inpaint_strength, force)
 
 
@@ -135,11 +150,74 @@ def _engine(key: str) -> Any:
             from remove_ai_watermarks.samsung_engine import SamsungEngine
 
             _engines[key] = SamsungEngine()
+        elif key == "jimeng_pill":
+            from remove_ai_watermarks.pill_engine import PillEngine
+
+            _engines[key] = PillEngine()
         else:  # pragma: no cover - guarded by the registry keys
             raise KeyError(key)
     return _engines[key]
 
 
+def inpaint_model_available() -> bool:
+    """True when any ONNX inpaint-model backend (MI-GAN or big-LaMa) can run."""
+    from remove_ai_watermarks import region_eraser
+
+    return region_eraser.migan_available() or region_eraser.lama_available()
+
+
+def preferred_inpaint_backend() -> str:
+    """Backend used by the inpaint fallback: MI-GAN (light, droplet-friendly, the
+    default) when its ONNX runtime is available, else cv2. big-LaMa is NOT auto-
+    selected -- it is a heavier explicit opt-in via ``erase --backend lama`` (both
+    models run on onnxruntime, so availability alone cannot express the user's
+    intent; the light model is the safe default)."""
+    from remove_ai_watermarks import region_eraser
+
+    return "migan" if region_eraser.migan_available() else "cv2"
+
+
+def resolve_removal_method(method: RemovalMethod, has_capture: bool) -> Literal["reverse-alpha", "inpaint"]:
+    """Resolve the requested method to a concrete one. A capture-less mark has no
+    alpha map, so it can only be inpainted -- even explicit ``reverse-alpha`` falls
+    back to inpaint there.
+
+    ``auto`` uses **reverse-alpha for capture marks** and **inpaint for capture-less**.
+    Reverse-alpha recovers the true pixels under the mark (measured cleaner than
+    MI-GAN inpaint on the capture marks -- doubao/gemini/jimeng -- especially on
+    structured backgrounds, and it needs no model / RAM), so it stays the default
+    where a capture exists; inpaint is reserved for marks that have no alpha map
+    (the Jimeng pill). ``--method inpaint`` still forces inpaint for anyone who
+    wants it."""
+    # A capture-less mark can only be inpainted; a capture mark inpaints only when
+    # explicitly asked (auto + reverse-alpha both recover the true pixels).
+    if method == "inpaint" or not has_capture:
+        return "inpaint"
+    return "reverse-alpha"
+
+
+def _inpaint_remove(mark: KnownMark, image: NDArray[Any], force: bool) -> tuple[NDArray[Any], Region | None]:
+    """Remove ``mark`` by inpainting its footprint: the NCC-aligned captured
+    silhouette (:meth:`footprint_mask`), erased with MI-GAN when its extra is
+    installed, else cv2 (see :func:`preferred_inpaint_backend`). No-op (returns a
+    copy) on a clean corner unless ``force``. Gated on the mark's trust-confidence
+    detection, so a clean image is never touched."""
+    from remove_ai_watermarks import region_eraser
+
+    det = mark.detect(image)
+    if not (det.detected or force):
+        return image.copy(), None
+    engine = _engine(mark.key)
+    fm = engine.footprint_mask(image, force=force)  # uniform signature; text/pill ignore force
+    if fm is None or not fm.any():
+        return image.copy(), None
+    if preferred_inpaint_backend() == "migan":
+        result = region_eraser.erase_migan(image, fm)
+    else:
+        result = region_eraser.erase_cv2(image, fm, radius=6)
+    return result, (det.region if det.detected else None)
+
+
 def _gemini_detect(image: NDArray[Any]) -> MarkDetection:
     d = _engine("gemini").detect_watermark(image)
     detected = bool(d.detected) and d.confidence >= _GEMINI_AUTO_MIN_CONF
@@ -207,11 +285,29 @@ def _text_mark(key: str, label: str, location: str) -> KnownMark:
     )
 
 
+# ── Capture-less mark: the Jimeng-basic "AI生成" pill (top-left, inpaint-only) ──
+# No alpha map exists, so removal is inpaint only (has_capture=False routes every
+# method to inpaint). Detection is edge-NCC of a synthetic silhouette; see pill_engine.
+def _pill_detect(image: NDArray[Any]) -> MarkDetection:
+    d = _engine("jimeng_pill").detect(image)
+    return MarkDetection("jimeng_pill", "Jimeng AI生成 pill", "top-left", d.detected, d.confidence, d.region)
+
+
+def _pill_noop_remove(
+    image: NDArray[Any], _im: InpaintMethod, _ip: bool, _st: float, _force: bool
+) -> tuple[NDArray[Any], Region | None]:
+    # Capture-less: reverse-alpha is impossible. resolve_removal_method routes every
+    # method to inpaint (_inpaint_remove), so this reverse-alpha slot is never reached;
+    # return an untouched copy defensively.
+    return image.copy(), None
+
+
 _REGISTRY: tuple[KnownMark, ...] = (
     KnownMark("gemini", "Google Gemini sparkle", "bottom-right", True, "reverse-alpha", _gemini_detect, _gemini_remove),
     _text_mark("doubao", "Doubao 豆包AI生成 text", "bottom-right"),
     _text_mark("jimeng", "Jimeng 即梦AI wordmark", "bottom-right"),
     _text_mark("samsung", "Samsung Galaxy AI text", "bottom-left"),
+    KnownMark("jimeng_pill", "Jimeng AI生成 pill", "top-left", True, "inpaint", _pill_detect, _pill_noop_remove, False),
 )
 
 
@@ -238,8 +334,7 @@ def detect_marks(image: NDArray[Any], *, include_explicit: bool = True) -> list[
 
     Returns one MarkDetection per scanned mark (``detected`` flags which fired).
     ``include_explicit=False`` scans only the ``in_auto`` marks -- the set used
-    by ``--mark auto``.
-    """
+    by ``--mark auto``."""
     return [m.detect(image) for m in _REGISTRY if include_explicit or m.in_auto]
 
 
@@ -247,3 +342,66 @@ def best_auto_mark(image: NDArray[Any]) -> MarkDetection | None:
     """The highest-confidence detected ``in_auto`` mark, or None if none fired."""
     fired = [d for d in detect_marks(image, include_explicit=False) if d.detected]
     return max(fired, key=lambda d: d.confidence) if fired else None
+
+
+def _keep_pill(image: NDArray[Any], keys: set[str], *, pill_metadata: bool) -> bool:
+    """Whether to auto-remove the capture-less 'AI生成' pill given the fired marks.
+
+    The pill detector is weak (~7% raw false-fire) and metadata confirms the platform,
+    not pill presence, so a naive metadata-OR-wordmark gate over-fires: on a 32k
+    real-upload corpus (2026-07) the metadata-only arm was only ~27% precise and its
+    false fires were textured ceilings/walls that inpaint visibly SMEARS. Two arms:
+      * bottom-right "★ 即梦AI" wordmark fired -> ~94% precise, and it survives
+        metadata-STRIPPED uploads: remove the pill unrestricted;
+      * metadata only (TC260 AIGC, no wordmark) -> remove ONLY when the top-left
+        footprint is flat enough for an invisible inpaint (``footprint_is_flat``),
+        so real flat-scene pills (and harmless flat false fires) are cleaned while
+        the damaging textured false fires are left untouched.
+    A Doubao image is TC260 too but is not Jimeng-basic, so the pill never rides on a
+    Doubao detection. No confirmation at all -> never remove (blocks false fires on
+    non-Jimeng content)."""
+    if "doubao" in keys:
+        return False
+    if "jimeng" in keys:
+        return True
+    if pill_metadata:
+        return bool(_engine("jimeng_pill").footprint_is_flat(image))
+    return False
+
+
+def remove_auto_marks(
+    image: NDArray[Any],
+    *,
+    pill_metadata: bool = False,
+    method: RemovalMethod = "auto",
+    inpaint_method: InpaintMethod = "ns",
+    inpaint: bool = True,
+    inpaint_strength: float = 0.85,
+) -> tuple[NDArray[Any], list[str]]:
+    """Remove EVERY detected ``in_auto`` mark in one pass, chaining the result.
+
+    Marks coexist in different corners -- a Jimeng-basic image carries BOTH the
+    top-left "AI生成" pill AND the bottom-right "★ 即梦AI" wordmark -- and their
+    confidences are on different scales, so ``best_auto_mark`` (single strongest)
+    would clean only one and leave the other (issue #54). Each mark re-detects at
+    its own corner on the progressively-cleaned image, so order does not matter.
+
+    The capture-less ``jimeng_pill`` has a weak edge-NCC detector (~7% raw
+    false-fire), so its removal is gated by ``_keep_pill`` (Jimeng-class confirmation
+    + a safe-inpaint check on the metadata-only arm; see that helper). Returns
+    ``(result, [labels removed])``; an empty list means nothing fired."""
+    fired = [d for d in detect_marks(image, include_explicit=False) if d.detected]
+    keys = {d.key for d in fired}
+    if "jimeng_pill" in keys and not _keep_pill(image, keys, pill_metadata=pill_metadata):
+        fired = [d for d in fired if d.key != "jimeng_pill"]
+    result = image
+    for det in fired:
+        result, _ = get_mark(det.key).remove(
+            result,
+            method=method,
+            inpaint_method=inpaint_method,
+            inpaint=inpaint,
+            inpaint_strength=inpaint_strength,
+            force=False,
+        )
+    return result, [d.label for d in fired]

tests/test_cli.py

@@ -459,19 +459,25 @@ class TestAllCommand:
 
     def test_all_visible_step_uses_registry(self, runner, sample_png, tmp_path):
         """Regression (#1): the `all` visible step must route through the registry
-        (best_auto_mark), so Doubao/Jimeng/Samsung text marks are handled -- not just
-        the Gemini sparkle via a hardcoded GeminiEngine."""
+        (remove_auto_marks), so Doubao/Jimeng/Samsung/pill marks are handled -- not
+        just the Gemini sparkle via a hardcoded GeminiEngine."""
         mock_cls, _mock_engine = _mock_invisible_engine()
         output = tmp_path / "clean.png"
+
+        def _fake_remove_auto(image, **kwargs):
+            return image, []
+
         with (
             patch("remove_ai_watermarks.cli.InvisibleEngine", mock_cls, create=True),
             patch("remove_ai_watermarks.invisible_engine.InvisibleEngine", mock_cls),
             patch("remove_ai_watermarks.invisible_engine.is_available", return_value=True),
-            patch("remove_ai_watermarks.watermark_registry.best_auto_mark", return_value=None) as mock_best,
+            patch(
+                "remove_ai_watermarks.watermark_registry.remove_auto_marks", side_effect=_fake_remove_auto
+            ) as mock_auto,
         ):
             result = runner.invoke(main, ["all", str(sample_png), "-o", str(output), "--force"])
         assert result.exit_code == 0, result.output
-        mock_best.assert_called()  # the registry auto-detector drove the visible pass
+        mock_auto.assert_called()  # the registry auto-detector drove the visible pass
 
     def test_all_skips_invisible_on_no_signal_but_succeeds(self, runner, sample_png, tmp_path):
         """P0#5: with no detectable invisible watermark and no --force, `all` skips

tests/test_doubao_engine.py

@@ -76,6 +76,24 @@ class TestDetect:
         solid = np.full_like(box, 255)
         assert _template_match_score(solid, _ALPHA_NATIVE_WIDTH) < DETECT_NCC_THRESHOLD
 
+    def test_small_image_guarded_from_false_positive(self):
+        """Below the minimum short side a tiny geometric shape spuriously NCC-matches
+        the CJK silhouette (2026-06-26 FP: a 48x48 app-icon chevron scored 0.41). The
+        size guard suppresses detection there. Bracket it: a real mark is detected at
+        native size, but the same content downscaled below the guard is not."""
+        w = h = _ALPHA_NATIVE_WIDTH
+        at = _alpha_template()
+        gw, gh = int(_ALPHA_WIDTH_FRAC * w), int(_ALPHA_HEIGHT_FRAC * w)
+        ax = w - int(_ALPHA_MARGIN_RIGHT_FRAC * w) - gw
+        ay = h - int(_ALPHA_MARGIN_BOTTOM_FRAC * w) - gh
+        amap = np.zeros((h, w), np.float32)
+        amap[ay : ay + gh, ax : ax + gw] = cv2.resize(at, (gw, gh))
+        a3 = amap[:, :, None]
+        wm = (a3 * np.array(_ALPHA_LOGO_BGR, np.float32) + (1 - a3) * 100.0).clip(0, 255).astype(np.uint8)
+        eng = DoubaoEngine()
+        assert eng.detect(wm).detected  # native: real mark detected
+        assert not eng.detect(cv2.resize(wm, (150, 150))).detected  # below guard: suppressed
+
 
 @pytest.mark.skipif(not SAMPLE.exists(), reason="sample image not present")
 class TestRealSample:

tests/test_gemini_engine.py

@@ -514,6 +514,38 @@ class TestSparkleFalsePositiveGate:
         assert det.confidence < 0.5
         assert not det.detected
 
+    def test_bright_background_low_gradient_match_demoted(self):
+        """Bright-background content FP (2026-06-26 landing-page reports: a snow+sky
+        photo and a white product render scored ~0.51). A bright background gives the
+        match a HIGH core-ring margin, so the margin gate cannot demote it -- but the
+        smooth blob lacks the crisp star edges of a real sparkle, so its GRADIENT NCC
+        is low. The gradient gate (``_SPARKLE_FP_GRAD``) demotes it. Reproduces the
+        regime with a heavily-blurred bright sparkle: high margin, low gradient, and a
+        pre-gate fusion above the 0.5 promote bar (so it WOULD have been detected).
+        """
+        size = 1400
+        config = get_watermark_config(size, size)
+        x, y = config.get_position(size, size)
+        alpha = self.engine.get_alpha_map(WatermarkSize.LARGE)
+        ah, aw = alpha.shape[:2]
+        img = np.full((size, size, 3), 110, dtype=np.float32)
+        a = np.clip(alpha * 1.6, 0.0, 1.0)[:, :, None]
+        img[y : y + ah, x : x + aw] = a * 255.0 + (1.0 - a) * img[y : y + ah, x : x + aw]
+        img = cv2.GaussianBlur(np.clip(img, 0, 255).astype(np.uint8), (39, 39), 0)
+        det = self.engine.detect_watermark(img)
+        # Pre-gate fusion clears the 0.5 promote bar (would have been detected)...
+        pre = det.spatial_score * 0.5 + det.gradient_score * 0.3 + det.variance_score * 0.2
+        assert pre > 0.5
+        # ...the OLD margin gate cannot catch it (bright background -> high margin)...
+        margin = self.engine._core_ring_margin(img, self.engine.get_interpolated_alpha(det.region[2]), det.region[:2])
+        assert margin is not None
+        assert margin >= self.engine._SPARKLE_FP_MARGIN
+        # ...but the gradient is low (smooth blob, not a crisp star)...
+        assert det.gradient_score < self.engine._SPARKLE_FP_GRAD
+        # ...so the gradient gate demotes it below the detection bar.
+        assert det.confidence < 0.5
+        assert not det.detected
+
 
 class TestCornerPromotion:
     """Issue #36: a small sparkle in the corner must not be lost to a larger decoy.

tests/test_inpaint_fallback.py

@@ -0,0 +1,134 @@
+"""Inpaint-fallback visible removal: method resolution, footprint masks, dispatch.
+
+The inpaint path erases the mark footprint (MI-GAN when the ``migan`` extra is
+installed, else cv2) instead of reverse-alpha, so a mark needs no captured alpha
+map for removal (only for the footprint silhouette). ``auto`` is deterministic:
+reverse-alpha for capture marks, inpaint for capture-less. These tests avoid any
+ONNX model download by pinning the backend to cv2 via ``preferred_inpaint_backend``;
+only pure cv2/numpy paths run.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from remove_ai_watermarks import watermark_registry as registry
+from remove_ai_watermarks.doubao_engine import DoubaoEngine
+from remove_ai_watermarks.gemini_engine import GeminiEngine
+
+
+def _compose_textmark(engine, bg: float = 120.0, w: int = 1024, h: int = 1024):
+    """Composite the engine's captured mark onto a flat ``bg`` at full opacity so
+    the mark is detectable. Returns ``(watermarked_uint8, (ax, ay, gw, gh))``."""
+    img = np.full((h, w, 3), float(bg), np.float32)
+    block, (ax, ay, gw, gh) = engine._fixed_alpha_map(img)
+    a = np.clip(block, 0.0, 0.99)[:, :, None]
+    logo = np.array(engine.config.alpha_logo_bgr, np.float32)
+    img[ay : ay + gh, ax : ax + gw] = img[ay : ay + gh, ax : ax + gw] * (1 - a) + logo * a
+    return np.clip(img, 0, 255).astype(np.uint8), (ax, ay, gw, gh)
+
+
+class TestResolveMethod:
+    @pytest.mark.parametrize("explicit", ["reverse-alpha", "inpaint"])
+    def test_explicit_passthrough(self, explicit: str) -> None:
+        # capture mark: the explicit method passes through unchanged
+        assert registry.resolve_removal_method(explicit, True) == explicit  # type: ignore[arg-type]
+        # capture-less: reverse-alpha is impossible, so it collapses to inpaint
+        expected = "inpaint" if explicit == "reverse-alpha" else explicit
+        assert registry.resolve_removal_method(explicit, False) == expected  # type: ignore[arg-type]
+
+    def test_auto_uses_reverse_alpha_for_capture_marks(self) -> None:
+        # auto is deterministic and model-independent: reverse-alpha where a capture
+        # exists (cleaner + lighter than inpaint), inpaint only where it does not.
+        assert registry.resolve_removal_method("auto", True) == "reverse-alpha"
+
+    def test_auto_uses_inpaint_for_capture_less(self) -> None:
+        assert registry.resolve_removal_method("auto", False) == "inpaint"
+
+
+class TestFootprintMask:
+    def test_textmark_footprint_geometry(self) -> None:
+        mask = DoubaoEngine().footprint_mask(np.full((1024, 1024, 3), 120, np.uint8))
+        assert mask is not None
+        assert mask.shape == (1024, 1024)
+        assert mask.dtype == np.uint8
+        assert mask.any()
+        # Doubao sits bottom-right: the mask mass is in the bottom-right quadrant.
+        ys, xs = np.where(mask > 0)
+        assert ys.mean() > 512
+        assert xs.mean() > 512
+
+    def test_textmark_small_image_returns_none(self) -> None:
+        assert DoubaoEngine().footprint_mask(np.full((20, 20, 3), 120, np.uint8)) is None
+
+    def test_gemini_footprint_needs_detection_or_force(self) -> None:
+        eng = GeminiEngine()
+        clean = np.full((1024, 1024, 3), 128, np.uint8)
+        assert eng.footprint_mask(clean) is None  # nothing detected -> no mask
+        forced = eng.footprint_mask(clean, force=True)  # default sparkle slot
+        assert forced is not None
+        assert forced.any()
+
+
+class TestInpaintDispatch:
+    """Force the cv2 backend (patch preferred_inpaint_backend) so no ONNX model
+    downloads; the dispatch/gating logic is backend-agnostic."""
+
+    def test_clean_image_is_untouched(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(registry, "preferred_inpaint_backend", lambda: "cv2")
+        img = np.full((1024, 1024, 3), 120, np.uint8)
+        out, region = registry.get_mark("doubao").remove(img, method="inpaint")
+        assert region is None
+        assert np.array_equal(out, img)  # not detected, not forced -> no-op
+
+    def test_forced_inpaint_edits_only_footprint(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(registry, "preferred_inpaint_backend", lambda: "cv2")
+        img, (ax, ay, gw, gh) = _compose_textmark(DoubaoEngine())
+        out, _ = registry.get_mark("doubao").remove(img, method="inpaint", force=True)
+        assert not np.array_equal(out[ay : ay + gh, ax : ax + gw], img[ay : ay + gh, ax : ax + gw])
+        assert np.array_equal(out[:200, :200], img[:200, :200])  # far corner untouched
+
+    def test_detected_inpaint_lowers_confidence(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(registry, "preferred_inpaint_backend", lambda: "cv2")
+        mark = registry.get_mark("doubao")
+        img, _ = _compose_textmark(DoubaoEngine())
+        before = mark.detect(img)
+        assert before.detected  # the composed mark is detectable
+        out, region = mark.remove(img, method="inpaint")
+        assert region is not None
+        assert mark.detect(out).confidence < before.confidence
+
+    def test_reverse_alpha_method_still_selectable(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(registry, "inpaint_model_available", lambda: True)  # would be inpaint on auto
+        img, _ = _compose_textmark(DoubaoEngine())
+        # explicit reverse-alpha bypasses the inpaint fallback even with a model present
+        out, region = registry.get_mark("doubao").remove(img, method="reverse-alpha")
+        assert region is not None
+        assert not np.array_equal(out, img)
+
+
+class TestBackendSelection:
+    """MI-GAN is the preferred inpaint backend (light, droplet-friendly); big-LaMa
+    is NOT auto-selected. cv2 is the floor when no ONNX model is present."""
+
+    def test_prefers_migan_when_available(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        from remove_ai_watermarks import region_eraser
+
+        monkeypatch.setattr(region_eraser, "migan_available", lambda: True)
+        assert registry.preferred_inpaint_backend() == "migan"
+
+    def test_cv2_when_no_model(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        from remove_ai_watermarks import region_eraser
+
+        monkeypatch.setattr(region_eraser, "migan_available", lambda: False)
+        assert registry.preferred_inpaint_backend() == "cv2"
+
+    def test_inpaint_model_available_reflects_either(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        from remove_ai_watermarks import region_eraser
+
+        monkeypatch.setattr(region_eraser, "migan_available", lambda: False)
+        monkeypatch.setattr(region_eraser, "lama_available", lambda: False)
+        assert not registry.inpaint_model_available()
+        monkeypatch.setattr(region_eraser, "lama_available", lambda: True)
+        assert registry.inpaint_model_available()

tests/test_jimeng_engine.py

@@ -86,6 +86,16 @@ class TestDetect:
         solid = np.full_like(box, 255)
         assert _template_match_score(solid, _ALPHA_NATIVE_WIDTH) < DETECT_NCC_THRESHOLD
 
+    def test_small_image_guarded_from_false_positive(self):
+        """Below the minimum short side a tiny geometric shape spuriously NCC-matches
+        the CJK silhouette (2026-06-26 FP: a 48x48 app-icon chevron scored 0.47). The
+        size guard suppresses detection there. Bracket it: a real mark is detected at
+        native size, but the same content downscaled below the guard is not."""
+        wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH)
+        eng = JimengEngine()
+        assert eng.detect(wm).detected  # native: real mark detected
+        assert not eng.detect(cv2.resize(wm, (150, 150))).detected  # below guard: suppressed
+
     def test_synthetic_mark_detected(self):
         """A watermark composed from the real alpha is detected at its threshold."""
         eng = JimengEngine()

tests/test_pill_engine.py

@@ -0,0 +1,160 @@
+"""Jimeng-basic 'AI生成' pill: capture-less mark (detect via synthetic silhouette
+edge-NCC, remove via inpaint). No model download -- cv2 fallback / pure logic only."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from PIL import Image, ImageDraw, ImageFont
+
+from remove_ai_watermarks import watermark_registry as registry
+from remove_ai_watermarks.pill_engine import _DETECT_THRESHOLD, PillEngine
+
+_FONT = "/System/Library/Fonts/STHeiti Medium.ttc"
+
+
+def _font_ok() -> bool:
+    try:
+        ImageFont.truetype(_FONT, 20)
+        return True
+    except Exception:
+        return False
+
+
+_HAS_FONT = _font_ok()
+_needs_font = pytest.mark.skipif(
+    not _HAS_FONT, reason="CJK font unavailable (compose helper needs it; asset is committed)"
+)
+
+
+def _compose_pill(w: int = 1200, h: int = 1600, bg: int = 150) -> np.ndarray:
+    """Composite a semi-transparent 'AI生成' pill top-left onto a flat BGR frame."""
+    img = Image.new("RGB", (w, h), (bg, bg, bg))
+    ov = Image.new("RGBA", (w, h), (0, 0, 0, 0))
+    d = ImageDraw.Draw(ov)
+    mw, mh = int(0.167 * w), int(0.09 * w)
+    mx, my = int(0.03 * w), int(0.02 * w)
+    d.rounded_rectangle([mx, my, mx + mw, my + mh], radius=mh // 3, outline=(255, 255, 255, 150), width=3)
+    font = ImageFont.truetype(_FONT, int(mh * 0.5))
+    d.text((mx + mw // 6, my + mh // 5), "AI生成", font=font, fill=(255, 255, 255, 170))
+    out = Image.alpha_composite(img.convert("RGBA"), ov).convert("RGB")
+    return np.asarray(out)[:, :, ::-1].copy()  # RGB->BGR
+
+
+class TestPillDetect:
+    @_needs_font
+    def test_detects_composited_pill(self) -> None:
+        det = PillEngine().detect(_compose_pill())
+        assert det.detected
+        assert det.confidence >= _DETECT_THRESHOLD
+
+    def test_clean_frame_does_not_fire(self) -> None:
+        clean = np.full((1600, 1200, 3), 150, np.uint8)
+        assert not PillEngine().detect(clean).detected
+
+    def test_small_image_no_fire(self) -> None:
+        assert not PillEngine().detect(np.full((40, 40, 3), 150, np.uint8)).detected
+
+
+def _textured_frame(w: int = 300, h: int = 400, bg: int = 150) -> np.ndarray:
+    """Flat frame with a high-frequency checkerboard over the top-left footprint,
+    so the pill footprint reads as TEXTURED (an inpaint there would smear)."""
+    img = np.full((h, w, 3), bg, np.uint8)
+    fx, fy, fw, fh = int(0.012 * w), int(0.006 * h), int(0.205 * w), int(0.115 * w)
+    yy, xx = np.mgrid[0:fh, 0:fw]
+    checker = (((xx // 3) + (yy // 3)) % 2 * 255).astype(np.uint8)
+    img[fy : fy + fh, fx : fx + fw] = checker[:, :, None]
+    return img
+
+
+class TestPillMask:
+    def test_footprint_mask_top_left_geometry(self) -> None:
+        mask = PillEngine().footprint_mask(np.full((1600, 1200, 3), 150, np.uint8))
+        assert mask is not None
+        assert mask.shape == (1600, 1200)
+        assert mask.any()
+        ys, xs = np.where(mask > 0)
+        # pill sits top-left: mask mass in the top-left quadrant
+        assert ys.mean() < 800
+        assert xs.mean() < 600
+
+
+class TestFootprintFlatness:
+    """The metadata-only pill arm removes only on a flat footprint (safe inpaint)."""
+
+    def test_flat_frame_is_flat(self) -> None:
+        assert PillEngine().footprint_is_flat(np.full((1600, 1200, 3), 150, np.uint8))
+
+    def test_textured_frame_is_not_flat(self) -> None:
+        eng = PillEngine()
+        assert not eng.footprint_is_flat(_textured_frame(1200, 1600))
+        # median-Sobel texture is well above the flat threshold on the checkerboard
+        assert eng.footprint_texture(_textured_frame(1200, 1600)) > 6.0
+
+
+class TestPillRegistry:
+    def test_pill_is_capture_less(self) -> None:
+        m = registry.get_mark("jimeng_pill")
+        assert m.has_capture is False
+
+    def test_capture_less_routes_every_method_to_inpaint(self) -> None:
+        # a capture-less mark cannot reverse-alpha; even explicit reverse-alpha -> inpaint
+        assert registry.resolve_removal_method("reverse-alpha", False) == "inpaint"
+        assert registry.resolve_removal_method("auto", False) == "inpaint"
+        assert registry.resolve_removal_method("inpaint", False) == "inpaint"
+
+
+class TestPillGate:
+    """Pill removal is gated (``_keep_pill``): the reliable bottom-right wordmark
+    removes it unrestricted, the metadata-only arm removes it ONLY on a flat footprint
+    (safe inpaint), Doubao/no-confirmation never remove it. Fakes detect_marks so no
+    image content is needed; cv2 backend so nothing downloads. Frame flatness matters
+    now, so tests pass a flat or a textured frame explicitly."""
+
+    @staticmethod
+    def _fakes(monkeypatch: pytest.MonkeyPatch, keys: set[str]) -> None:
+        from remove_ai_watermarks.watermark_registry import MarkDetection
+
+        labels = {
+            "doubao": "Doubao 豆包AI生成 text",
+            "jimeng": "Jimeng 即梦AI wordmark",
+            "jimeng_pill": "Jimeng AI生成 pill",
+        }
+        monkeypatch.setattr(registry, "preferred_inpaint_backend", lambda: "cv2")
+        monkeypatch.setattr(
+            registry,
+            "detect_marks",
+            lambda image, *, include_explicit=True: [
+                MarkDetection(k, labels[k], "loc", True, 0.6, (10, 10, 40, 40)) for k in keys
+            ],
+        )
+
+    def test_pill_kept_with_metadata_on_flat_footprint(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        # metadata-only + flat background -> safe inpaint, remove
+        self._fakes(monkeypatch, {"jimeng_pill"})
+        _, removed = registry.remove_auto_marks(np.full((400, 300, 3), 150, np.uint8), pill_metadata=True)
+        assert "Jimeng AI生成 pill" in removed
+
+    def test_pill_dropped_with_metadata_on_textured_footprint(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        # metadata-only + textured background (ceiling-like) -> inpaint would smear, skip
+        self._fakes(monkeypatch, {"jimeng_pill"})
+        _, removed = registry.remove_auto_marks(_textured_frame(), pill_metadata=True)
+        assert "Jimeng AI生成 pill" not in removed
+
+    def test_pill_kept_via_wordmark_ignores_texture(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        # wordmark confirmation (~94% precise, survives metadata stripping) is NOT
+        # texture-gated: a wordmark-confirmed pill is removed even on a textured frame
+        self._fakes(monkeypatch, {"jimeng", "jimeng_pill"})
+        _, removed = registry.remove_auto_marks(_textured_frame(), pill_metadata=False)
+        assert "Jimeng AI生成 pill" in removed
+
+    def test_pill_dropped_without_metadata_or_wordmark(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        self._fakes(monkeypatch, {"jimeng_pill"})
+        _, removed = registry.remove_auto_marks(np.full((400, 300, 3), 150, np.uint8), pill_metadata=False)
+        assert "Jimeng AI生成 pill" not in removed
+
+    def test_pill_dropped_on_doubao_even_with_metadata(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        self._fakes(monkeypatch, {"doubao", "jimeng_pill"})
+        _, removed = registry.remove_auto_marks(np.full((400, 300, 3), 150, np.uint8), pill_metadata=True)
+        assert "Doubao 豆包AI生成 text" in removed
+        assert "Jimeng AI生成 pill" not in removed

tests/test_region_eraser.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 import numpy as np
 import pytest
 
-from remove_ai_watermarks.region_eraser import boxes_to_mask, erase, lama_available
+from remove_ai_watermarks.region_eraser import boxes_to_mask, erase, lama_available, migan_available
 
 
 class TestBoxesToMask:
@@ -134,3 +134,74 @@ class TestLamaChannelHandling:
         out = erase(bgra, boxes=[(40, 40, 20, 20)], backend="lama")
         assert out.shape == bgra.shape
         assert np.array_equal(out[..., 3], bgra[..., 3])  # alpha carried through unchanged
+
+
+class TestMiganBackend:
+    def test_migan_raises_when_unavailable(self):
+        img = np.full((100, 100, 3), 50, np.uint8)
+        if migan_available():
+            pytest.skip("onnxruntime installed; cannot test the unavailable path")
+        with pytest.raises(RuntimeError, match="onnxruntime"):
+            erase(img, boxes=[(10, 10, 20, 20)], backend="migan")
+
+
+class TestMiganWrapper:
+    """erase_migan without the real model: fake session returns a solid-red field
+    and captures the fed mask. Exercises the mask-polarity inversion, masked-only
+    compositing, and grayscale/BGRA channel handling."""
+
+    captured: dict
+
+    @pytest.fixture
+    def _fake_migan(self, monkeypatch: pytest.MonkeyPatch):
+        from remove_ai_watermarks import region_eraser
+
+        self.captured = {}
+
+        class _In:
+            def __init__(self, name: str):
+                self.name = name
+
+        class _FakeSession:
+            def __init__(self, outer):
+                self.outer = outer
+
+            def get_inputs(self):
+                return [_In("image"), _In("mask")]
+
+            def run(self, _outputs, feeds):
+                self.outer.captured["mask"] = feeds["mask"]
+                img = feeds["image"]  # (1,3,H,W) RGB
+                red = np.zeros_like(img)
+                red[:, 0] = 255  # pure red in RGB
+                return [red]
+
+        monkeypatch.setattr(region_eraser, "migan_available", lambda: True)
+        monkeypatch.setattr(region_eraser, "_get_migan_session", lambda: _FakeSession(self))
+
+    @pytest.mark.usefixtures("_fake_migan")
+    def test_composites_only_masked_region_and_inverts_mask(self):
+        img = np.full((100, 100, 3), 120, np.uint8)  # BGR
+        out = erase(img, boxes=[(40, 40, 20, 20)], backend="migan", dilate=0)
+        # inside the box -> red (BGR (0,0,255)); outside -> untouched
+        assert tuple(int(v) for v in out[50, 50]) == (0, 0, 255)
+        assert np.array_equal(out[:30, :30], img[:30, :30])
+        # mask fed to MI-GAN is inverted: 0 (hole) inside the box, 255 (known) outside
+        m = self.captured["mask"][0, 0]
+        assert m[50, 50] == 0
+        assert m[10, 10] == 255
+
+    @pytest.mark.usefixtures("_fake_migan")
+    def test_grayscale_2d_does_not_raise(self):
+        gray = np.full((100, 100), 120, np.uint8)
+        out = erase(gray, boxes=[(40, 40, 20, 20)], backend="migan", dilate=0)
+        assert out.ndim == 2
+        assert out.shape == gray.shape
+
+    @pytest.mark.usefixtures("_fake_migan")
+    def test_bgra_preserves_alpha(self):
+        bgra = np.full((100, 100, 4), 120, np.uint8)
+        bgra[..., 3] = 200
+        out = erase(bgra, boxes=[(40, 40, 20, 20)], backend="migan", dilate=0)
+        assert out.shape == bgra.shape
+        assert np.array_equal(out[..., 3], bgra[..., 3])

tests/test_samsung_engine.py

@@ -87,6 +87,16 @@ class TestDetect:
         solid = np.full_like(box, 255)
         assert _template_match_score(solid, _ALPHA_NATIVE_WIDTH) < DETECT_NCC_THRESHOLD
 
+    def test_small_image_guarded_from_false_positive(self):
+        """Below the minimum short side a tiny geometric shape spuriously NCC-matches
+        the glyph silhouette (the 2026-06-26 small-icon FP class). The size guard
+        suppresses detection there. Bracket it: a real mark is detected at native
+        size, but the same content downscaled below the guard is not."""
+        wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, int(_ALPHA_NATIVE_WIDTH * 1.33))
+        eng = SamsungEngine()
+        assert eng.detect(wm).detected  # native: real mark detected
+        assert not eng.detect(cv2.resize(wm, (150, 150))).detected  # below guard: suppressed
+
     def test_synthetic_mark_detected(self):
         """A watermark composed from the real alpha is detected at its threshold."""
         eng = SamsungEngine()

tests/test_watermark_registry.py

@@ -14,14 +14,17 @@ DOUBAO_SAMPLE = Path(__file__).resolve().parents[1] / "data" / "samples" / "doub
 
 class TestCatalog:
     def test_keys(self):
-        assert reg.mark_keys() == ["gemini", "doubao", "jimeng", "samsung"]
+        assert reg.mark_keys() == ["gemini", "doubao", "jimeng", "samsung", "jimeng_pill"]
 
     def test_all_in_auto(self):
         assert all(m.in_auto for m in reg.known_marks())
 
-    def test_recovery_is_reverse_alpha(self):
-        # Every catalogued mark is removed by exact reverse-alpha (no inpaint).
-        assert all(m.recovery == "reverse-alpha" for m in reg.known_marks())
+    def test_recovery(self):
+        # Capture marks recover by reverse-alpha; the capture-less pill is inpaint-only.
+        by_key = {m.key: m for m in reg.known_marks()}
+        assert all(by_key[k].recovery == "reverse-alpha" for k in ("gemini", "doubao", "jimeng", "samsung"))
+        assert by_key["jimeng_pill"].recovery == "inpaint"
+        assert by_key["jimeng_pill"].has_capture is False
 
     def test_locations(self):
         by_key = {m.key: m for m in reg.known_marks()}
@@ -29,6 +32,7 @@ class TestCatalog:
         assert by_key["doubao"].location == "bottom-right"
         assert by_key["jimeng"].location == "bottom-right"
         assert by_key["samsung"].location == "bottom-left"
+        assert by_key["jimeng_pill"].location == "top-left"
 
     def test_get_mark_unknown_raises(self):
         with pytest.raises(KeyError):
@@ -39,7 +43,7 @@ class TestScan:
     def test_detect_marks_scans_all(self):
         img = np.zeros((256, 256, 3), np.uint8)
         keys = {d.key for d in reg.detect_marks(img)}
-        assert keys == {"gemini", "doubao", "jimeng", "samsung"}
+        assert keys == {"gemini", "doubao", "jimeng", "samsung", "jimeng_pill"}
 
     def test_blank_image_no_auto_mark(self):
         assert reg.best_auto_mark(np.zeros((256, 256, 3), np.uint8)) is None

uv.lock

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