chore(release): v0.11.3

extract_c2pa_info now uses the c2pa-python Reader first (any container, whole
manifest store incl. ingredient manifests), falling back to the hand-rolled caBX
parser for blobs the validator rejects (synthetic/partial, broken wheel). The
issuer/source-type/SynthID/soft-binding registry scan is shared by both paths
(_populate_registry_fields), so the return-dict contract is unchanged. Also
replaces the dead `from c2pa import has_c2pa_metadata` import in metadata.py with
a real Reader presence check. c2pa-python added as a core dep (MIT/Apache, ~+5MB
RSS, no torch; wheels cover the CI matrix).

Validated on the full local spaces corpus (25,725 imgs): 0 regressions; 384
manifests newly parsed (379 non-PNG JPEG/WebP + 2 PNGs the byte-scanner missed);
3 false Adobe/Microsoft->Google attributions fixed via real-manifest parsing.

The docs/module-internals.md section for this change already landed in 41f6797.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

CLAUDE.md

@@ -2,11 +2,24 @@
 
 You are a **principal Python engineer** maintaining a CLI tool and library for removing visible and invisible AI watermarks from images.
 
+## Scope and non-goals
+
+The mission is removing **AI-provenance watermarks** that a platform stamps onto content the user generated themselves — SynthID, the Gemini / Nano Banana sparkle, the Doubao / Jimeng / Samsung visible AI labels, the Chinese TC260 "由…AI生成" label, and C2PA / IPTC / EXIF "Made with AI" metadata. The point is user autonomy over their own generated output.
+
+It deliberately does **not** remove watermarks that protect someone else's paid or copyrighted content — stock-agency overlays (Shutterstock, Getty, iStock, Adobe Stock), classifieds-site marks, or any tiled / diagonal "preview" watermark whose job is to gate a purchase. Stripping those makes a paid resource free off someone else's work; out of scope **by principle, not by technical difficulty**. The line: a visible mark is in scope when it labels the user's **own** AI generation, and out of scope when it protects a **third party's paid asset**.
+
+Consequences for contributors (do not drift back into the stock niche just because it is technically feasible):
+- Do not add stock / agency / classifieds watermark removal to `watermark_registry.py` or the eraser, and do not build tiled-overlay or multi-image watermark-estimation features aimed at them.
+- `erase --region` stays a generic **user-driven** tool (the user points at their own object); do not ship an *automatic* stock-watermark detector/remover on top of it.
+- New visible-mark templates are for **AI-generation labels only**.
+
+(Established 2026-06-13 by user instruction: "Я пытаюсь сделать платные ресурсы бесплатными — это не то, против чего мы боремся.")
+
 ## 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`. **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` (default `controlnet`), `--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), and `--adaptive-polish/--no-adaptive-polish` (**ON by default**; 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).
-- `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`.
+- `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.
 - `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)
@@ -37,7 +50,7 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r
 
 Compact map. The full per-module detail (design decisions, tuned thresholds, calibration history, incident records, and the regression-guard map) lives in `docs/module-internals.md` — **read the relevant section there before changing any module below.**
 
-- `noai/c2pa.py` — PNG caBX chunk parser (`extract_c2pa_chunk` / `has_c2pa_metadata` / `extract_c2pa_info`). Do not reimplement chunk parsing; chunk reads are clamped to the remaining file size by design.
+- `noai/c2pa.py` — C2PA reading. `extract_c2pa_info(path)` uses the official **c2pa-python `Reader`** first (core dep, any container; `read_manifest_store_json` returns the WHOLE store JSON — active + ingredient manifests — so an AI marker on a parent manifest is seen), and falls back to the hand-rolled caBX/CBOR parser (`has_c2pa_metadata` / `extract_c2pa_chunk` / `_extract_c2pa_info_png`) for synthetic/partial blobs the validator rejects or a broken/absent wheel. The registry scan (issuer / source-type / SynthID / soft-binding) is shared by both paths via `_populate_registry_fields`, so the return-dict shape is identical. Do not reimplement chunk parsing; chunk reads are clamped to the remaining file size by design. `extract_c2pa_chunk`/`inject_c2pa_chunk` stay PNG-only (raw caBX bytes, test/extractor use).
 - `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`).
 - `identify.py` — aggregates every locally-readable signal into one `ProvenanceReport`; `is_ai_generated` is True or None, never asserted False. `import identify` is deliberately light (lazy `noai/__init__`, fits a 512 MB host) — keep heavy imports out. 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).

README.md

@@ -19,6 +19,12 @@ If this tool saves you time, consider [sponsoring its development](https://githu
 
 > **Intended for lawful use only.** Publishing and running this software is lawful; responsibility for any downstream use, and for compliance with local law, rests entirely with the user. Some jurisdictions restrict removing an AI label as such (see [Legal](#legal)). The authors do not condone use for deception, fraud, or any unlawful activity.
 
+## Scope
+
+This tool removes **AI-provenance watermarks** that a platform stamps onto content **you generated yourself** — SynthID, the Gemini / Nano Banana sparkle, the Doubao / Jimeng / Samsung visible AI labels, the Chinese TC260 "由…AI生成" label, and C2PA / IPTC / EXIF "Made with AI" metadata. The point is your autonomy over your own output.
+
+It does **not** target watermarks that protect someone else's paid or copyrighted content — stock-agency overlays (Shutterstock, Getty, iStock, Adobe Stock), classifieds-site marks, or any tiled "preview" watermark whose job is to gate a purchase. Removing those is out of scope by design. `erase` is a generic, user-driven region tool for your own objects, not an automatic stock-watermark remover.
+
 ## Features
 
 - **Visible watermark removal** — a registry of known marks in their usual places: the Gemini / Nano Banana 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). Each is removed by **reverse-alpha blending** against a captured alpha map (`original = (wm − α·logo)/(1−α)`), recovering the true pixels rather than inpainting a guess. The Gemini sparkle recovers cleanly on its own on bright backgrounds; it adapts the alpha to each image's sparkle opacity, so a more-opaque-than-captured sparkle is still fully removed (and on a dark background, where the fixed alpha would over-subtract and leave a dark spot, it automatically inpaints the small sparkle footprint instead); the Doubao, Jimeng, and Samsung text marks re-rasterize slightly per image, so a thin residual inpaint over the glyph footprint clears the leftover edges (the alpha maps are reproducibly rebuilt from controlled captures by `scripts/visible_alpha_solve.py`). Fast, offline, no GPU. `visible --mark auto` finds and removes the strongest detected mark. (For arbitrary logos/objects, see `erase`.)
@@ -333,6 +339,9 @@ remove-ai-watermarks identify image.png
 # strongest known mark (Gemini sparkle / Doubao "豆包AI生成" / Jimeng "即梦AI" /
 # Samsung Galaxy AI "Contenuti generati dall'AI"); force one with
 # --mark gemini / doubao / jimeng / samsung. Removed by reverse-alpha (true-pixel recovery).
+# If no known visible mark is found, it writes no output and exits 2 (not 0),
+# pointing you to `all` (for an invisible/metadata mark) or `erase` (for an
+# arbitrary logo) instead of handing back the unchanged image.
 remove-ai-watermarks visible image.png -o clean.png
 
 # Erase arbitrary region(s) — universal, any logo/watermark/object, any position.

docs/known-limitations.md

@@ -71,11 +71,11 @@ Generic HuggingFace detectors (`Organika/sdxl-detector` Swin Transformer, `umm-m
 
 ## Default strength is vendor-adaptive, one ladder for both pipelines
 
-**DEFAULT STRENGTH IS VENDOR-ADAPTIVE, ONE LADDER FOR BOTH PIPELINES (raised + unified 2026-06-09; vendor-adaptive since 2026-06-01, SUPERSEDES every fixed-default claim in this bullet and the next).**
+**DEFAULT STRENGTH IS VENDOR-ADAPTIVE, ONE LADDER FOR BOTH PIPELINES (LOWERED 2026-06-14; raised + unified 2026-06-09; vendor-adaptive since 2026-06-01, SUPERSEDES every fixed-default claim in this bullet and the next).**
 
-`resolve_strength(strength, vendor)` + `vendor_for_strength(path)` (`watermark_profiles.py`) read the C2PA issuer (`metadata.synthid_source`) on the ORIGINAL input and pick `OPENAI_STRENGTH` **0.20** / `GEMINI_STRENGTH` **0.30** / `UNKNOWN_STRENGTH` **0.30** when `--strength` is unset; explicit `--strength` always wins.
+`resolve_strength(strength, vendor)` + `vendor_for_strength(path)` (`watermark_profiles.py`) read the C2PA issuer (`metadata.synthid_source`) on the ORIGINAL input and pick `OPENAI_STRENGTH` **0.10** / `GEMINI_STRENGTH` **0.15** / `UNKNOWN_STRENGTH` **0.15** when `--strength` is unset; explicit `--strength` always wins.
 
-**The SAME ladder applies to BOTH pipelines** (`sdxl` and `controlnet`) -- these are the 2026-06-04 Modal-cert controlnet floors.
+**The SAME ladder applies to BOTH pipelines** (`sdxl` and `controlnet`). **2026-06-14: lowered from the 2026-06-04 cert floors (OpenAI 0.20 / Google 0.30) back toward the original 2026-06-01 study (OpenAI ~0.05-0.10 / Google 0.15).** A re-test on the deployed Modal controlnet worker cleared SynthID on the oracle at OpenAI 0.10 (2 photoreal, 1402/1448 px) and Google 0.15 (2 NATIVE 2816x1536 images -- retiring the "native ~2816 likely needs >=0.30" guess), while a pixel sweep showed 0.20/0.30 over-regenerated for no efficacy gain (Google MAE -20% at 0.15). See `watermark_profiles.py` "Data basis". **CAVEATS that stand:** (1) removal near this floor is SEED-NON-DETERMINISTIC (the 2026-06-09 finding below) -- a SERVICE on this ladder must pin a fixed, oracle-verified seed, not rely on a random one; (2) the re-test is n=2 per vendor on photoreal/landscape, NOT flat graphics (the `sdxl` weak spot), so raise `--strength` if an oracle reads SynthID on a flat output.
 
 **Why one ladder (NOT a per-pipeline split):** the cert was run on controlnet and does NOT transfer to `sdxl` by symmetry (opposite hard cases -- controlnet leaves SynthID on photoreal, `sdxl` on flat graphics), BUT on its OWN hard case (flat fills) `sdxl` is the WEAKER remover (plain img2img barely perturbs a flat region at low strength), so it needs AT LEAST controlnet's strength -- hence the certified floor is the right floor for `sdxl` too. It is a MARGIN argument for `sdxl`, not a fresh certification (no local SynthID detector to self-verify); raise `--strength` if an oracle still reads a flat `sdxl` output. The higher strength costs little quality because `controlnet` is now the default pipeline AND the only `--auto` pick, so `sdxl` is reached only via an explicit `--pipeline sdxl` (a deliberate opt-down for inputs without faces/text), where over-regeneration has nothing to damage. (A short-lived per-pipeline split ladder -- `sdxl` 0.15/0.20 vs controlnet 0.20/0.30 -- existed on 2026-06-09 before being unified the same day; the `resolve_strength` `pipeline` param and the `CONTROLNET_*_STRENGTH` constants were removed.) The CLI detects the vendor from the pristine source (before the visible pass / metadata-strip removes C2PA from the temp file) and passes it to display calls so display and execution agree; `cmd_invisible`/`cmd_all`/`batch` thread `vendor`.
 

docs/module-internals.md

@@ -11,7 +11,7 @@ module.
 
 ## `noai/c2pa.py`
 
-`noai/c2pa.py` — PNG chunk parser; use `extract_c2pa_chunk(path)` to get raw caBX payload, `has_c2pa_metadata(path)` to detect. Do not reimplement chunk parsing. `extract_c2pa_info(path)` sets `synthid_watermark`/`synthid_vendors` when the manifest is signed by a SynthID-using vendor, and `soft_binding`/`soft_binding_vendors` when a `c2pa.soft-binding` `alg` names a forensic-watermark vendor (`soft_binding_vendors_in(buffer)` is the shared byte-scan, used by both the PNG parser and the non-PNG binary path). PNG/caBX chunk reads are clamped to the remaining file size (`safe_length = min(length, remaining)`; skipped chunks use seek) so a malformed huge `length` cannot drive a multi-GB allocation (shared safety discipline matching `isobmff.scan_c2pa_region`).
+`noai/c2pa.py` — C2PA reading, **official c2pa-python `Reader` first, hand-rolled parser as fallback** (migrated 2026-06-18; the official lib is a core dep, MIT/Apache, spec-tracking). `read_manifest_store_json(path)` runs `Reader.try_create` with a default `Context` (NO trust enforcement — we report what is in the file, we do not gate on cert trust) and returns the **whole** manifest-store JSON (every manifest plus ingredient manifests); it is memoized per (path, mtime) (`lru_cache(maxsize=8)`) because one `identify`/`get_ai_metadata` call invokes the structured parser ~3x on the same file. `extract_c2pa_info(path)` builds its dict from that store JSON (`_info_from_store_json`: structured `claim_generator` from the active manifest's `claim_generator` / `claim_generator_info[].name`, `timestamp` from `signature_info.time`) and falls back to the legacy caBX parser (`_extract_c2pa_info_png`) when the reader is unavailable (broken/absent wheel, `reader_available()` False) or finds no parseable manifest (synthetic/partial test blobs, the inject round-trip's re-stitched chunk). **Both paths share `_populate_registry_fields(buf, info)`** — the issuer / AI-tool / action / source-type / SynthID / soft-binding registry byte-scan applied to the store JSON (reader path) or the raw caBX bytes (fallback) — so the return-dict shape is identical and the registry stays the single source of truth. Whole-store scanning is load-bearing: a ChatGPT *edit* of a Sora generation keeps `trainedAlgorithmicMedia` + issuer "OpenAI" on the **parent/ingredient** manifest, not the active "opened" one (the active manifest's `signature_info.issuer` is "OpenAI", `common_name` "Truepic Lens CLI in Sora", so the issuer field now reads "OpenAI, Truepic" — first-match-wins platform attribution still resolves OpenAI). `extract_c2pa_info` now also serves non-PNG containers (JPEG/AVIF/MP4) structurally via the reader; the consumers (`identify`, `synthid_source`, `get_ai_metadata`) already merge `info OR byte-scan`, so this strictly upgrades the non-PNG path with no double-counting. `synthid_watermark`/`synthid_vendors` is set when the manifest is signed by a SynthID-using vendor on AI content; `soft_binding`/`soft_binding_vendors` when a `c2pa.soft-binding` `alg` names a forensic-watermark vendor (`soft_binding_vendors_in(buffer)` is the shared byte-scan, used by both paths and the non-PNG binary path). `extract_c2pa_chunk` / `inject_c2pa_chunk` / `has_c2pa_metadata` stay the PNG caBX byte tools (raw-chunk extraction for `extractor.py`, test injection, fallback detection). PNG/caBX chunk reads are clamped to the remaining file size (`safe_length = min(length, remaining)`; skipped chunks use seek) so a malformed huge `length` cannot drive a multi-GB allocation (shared safety discipline matching `isobmff.scan_c2pa_region`). Regression-guarded by `tests/test_noai.py::TestC2PARealSamples::{test_extract_info_uses_reader_store,test_fallback_to_png_parser_when_reader_unavailable}`.
 
 ## `noai/constants.py`
 
@@ -29,7 +29,7 @@ module.
 
 **Visible-mark detection** (`check_visible`, signals `visible_sparkle` / `visible_doubao` / `visible_jimeng` / `visible_samsung`): the Gemini sparkle keeps its own file-level path (`_visible_sparkle` → `gemini_engine.detect_sparkle_confidence`, promoted only at confidence ≥ `_SPARKLE_THRESHOLD` 0.5; corpus-tuned to separate Gemini sparkles ≥0.56 from non-sparkle ≤0.49), while Doubao/Jimeng/Samsung reuse the registry detectors (`_visible_text_marks` → `watermark_registry`, iterating `_VISIBLE_MARK_PLATFORM`), each gated by its own engine NCC threshold via `MarkDetection.detected` (Doubao 0.4, Jimeng 0.45, Samsung 0.4). Doubao/Jimeng are normally also caught by the TC260 AIGC metadata label and Samsung by its C2PA + `genAIType` marker, so the visible path is their stripped-metadata fallback. Visible marks set `platform` only when no harder signal already did, and (like the sparkle) are excluded from integrity-clash vendor claims. The cv2 dependency lives in the engines, not here.
 
-**`import identify` is deliberately light** (~21 MB; ~36 MB with cv2 loaded by a visible-mark run, ~106 MB for a full `check_visible` run): it imports only the pure `noai.c2pa`/`noai.constants` submodules, and `noai/__init__` is lazy (see "Test and lint"), so torch/diffusers are NOT pulled at import even in a full `gpu`/`detect` install — fits a 512 MB host. The heavy paths are opt-in: `check_invisible=True` needs the `detect`/`trustmark` extras (each pulls **torch**; TrustMark also **downloads weights**), so on a core-only deploy leave `check_invisible` off (it is a no-op there anyway). Before the lazy `__init__`, the mere presence of torch in the env inflated `import identify` to ~420 MB.
+**`import identify` is deliberately light** (~26 MB; ~36 MB with cv2 loaded by a visible-mark run, ~106 MB for a full `check_visible` run): it imports the `noai.c2pa`/`noai.constants` submodules, and `noai/__init__` is lazy (see "Test and lint"), so torch/diffusers are NOT pulled at import even in a full `gpu`/`detect` install — fits a 512 MB host. `noai.c2pa` does eagerly import the **c2pa-python** binary (Rust + cryptography, ~+5 MB RSS, no torch) for the primary `Reader` path — light enough to stay on the dependency-light host; a broken/absent wheel degrades to the byte-scan parser (`reader_available()` False). The heavy paths are opt-in: `check_invisible=True` needs the `detect`/`trustmark` extras (each pulls **torch**; TrustMark also **downloads weights**), so on a core-only deploy leave `check_invisible` off (it is a no-op there anyway). Before the lazy `__init__`, the mere presence of torch in the env inflated `import identify` to ~420 MB.
 
 **C2PA platform attribution is device-token-first, issuer-scan fallback** (`_device_platform` scans manifest bytes for `_DEVICE_C2PA_PLATFORM` tokens, then `_attribute_platform`/`_ISSUER_PLATFORM`).
 
@@ -103,6 +103,8 @@ The 11 survivors are near-white ill-conditioning (reverse-alpha divides by `1-a`
 
 `_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.
 
+**`_apply_reverse_alpha` runs on the glyph crop only:** `amap` is zero outside the glyph `region` (x, y, w, h), so the blend is a no-op there (`(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. `remove_watermark_reverse_alpha` passes the `region` each `_fixed/_aligned_alpha_map` returns.
+
 ## `doubao_engine.py`
 
 `doubao_engine.py` — **a thin `_text_mark_engine.TextMarkEngine` subclass (config only) since 2026-06-09.** visible Doubao "豆包AI生成" remover/detector (cv2/numpy, no GPU). `DoubaoEngine.locate` anchors a bottom-right box by **geometry** (mark scales with image WIDTH), `extract_mask` pulls the light, low-chroma glyphs (the detection candidate) using a per-pixel channel-spread proxy `sat = roi.max(axis=2) - roi.min(axis=2)` (no HSV conversion). `detect` is **shape-consistent**: it matches the bundled alpha glyph silhouette (`assets/doubao_alpha.png`) against the candidate via zero-mean normalized correlation (`_template_match_score`, cv2 `TM_CCOEFF_NORMED`), gated at `DETECT_NCC_THRESHOLD` 0.4 over a small `DETECT_MIN_COVERAGE` floor. Keying on glyph SHAPE (not coverage heuristics) fixed #23 (corpus FP 7/1243).

docs/synthid.md

@@ -453,9 +453,18 @@ study (section 2.2) gives empirical floors:
 
 The default is **vendor-adaptive** (`watermark_profiles.resolve_strength` +
 `vendor_for_strength`): the tool reads the C2PA issuer on the original input and picks
-`OPENAI_STRENGTH` 0.20 / `GEMINI_STRENGTH` 0.30 / `UNKNOWN_STRENGTH` 0.30. **The SAME
-ladder applies to both pipelines** (`sdxl` and `controlnet`) -- these are the
-oracle-certified controlnet floors (§5.5, the 2026-06-04 Modal cert). Why one ladder
+`OPENAI_STRENGTH` 0.10 / `GEMINI_STRENGTH` 0.15 / `UNKNOWN_STRENGTH` 0.15 **(LOWERED
+2026-06-14 from the 2026-06-04 cert floors 0.20/0.30/0.30)**. **The SAME ladder applies
+to both pipelines** (`sdxl` and `controlnet`). The 2026-06-14 re-test on the deployed
+Modal controlnet worker (v0.10.0) cleared SynthID on the oracle at OpenAI 0.10 (2
+photoreal) and Google 0.15 (2 NATIVE 2816x1536, contradicting the "native >= 0.30" guess
+on line above), and a pixel sweep showed 0.20/0.30 over-regenerated for no efficacy gain.
+**This re-opens a genuine tension with the 2026-06-04 pass, which found photoreal STILL
+detected after controlnet at 0.10/0.15 (lines above):** either the v0.10.0 controlnet
+default improved the floor, or n=2 landed on the lucky side of the seed-non-determinism
+(§5.5). So a SERVICE on this ladder MUST pin a fixed, oracle-verified seed (not random),
+and flat-graphic hard cases (NOT in the n=2 re-test) still need a per-content oracle
+recheck -- raise `--strength` there. The prior cert floors are the §5.5 record. Why one ladder
 covers plain `sdxl` too: the certification was run on controlnet and does NOT transfer
 by symmetry (the two pipelines have OPPOSITE hard cases -- controlnet leaves SynthID on
 photoreal, `sdxl` on flat graphics, the §5.1 content-x-pipeline table), BUT on its own

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "remove-ai-watermarks"
-version = "0.11.1"
+version = "0.11.3"
 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"
@@ -51,6 +51,13 @@ dependencies = [
     "opencv-python-headless>=4.8.0",
     "click>=8.0.0",
     "python-dotenv>=1.0.0",
+    # Official C2PA reader (Content Authenticity Initiative, MIT/Apache-2.0). The
+    # primary, spec-tracking manifest parser for the identify/metadata path; the
+    # hand-rolled caBX/CBOR scanner in noai/c2pa.py is kept only as a fallback for
+    # synthetic/partial blobs the validator rejects. Binary wheel (Rust), but the
+    # import is light (no torch/numpy) so it fits the dependency-light identify
+    # host. Prebuilt wheels cover the full CI matrix (linux/macos/windows).
+    "c2pa-python>=0.35.0",
 ]
 
 [project.optional-dependencies]

src/remove_ai_watermarks/__init__.py

@@ -11,4 +11,4 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
 
 
-__version__ = "0.11.1"
+__version__ = "0.11.3"

src/remove_ai_watermarks/_text_mark_engine.py

@@ -302,11 +302,28 @@ class TextMarkEngine:
         amap[ay : ay + gh, ax : ax + gw] = cv2.resize(at, (gw, gh), interpolation=cv2.INTER_LINEAR)
         return amap, (ax, ay, gw, gh)
 
-    def _apply_reverse_alpha(self, image: NDArray[Any], amap: NDArray[Any]) -> NDArray[Any]:
-        """Invert the alpha blend with ``amap``: ``original = (wm - a*logo)/(1-a)``."""
-        a3 = np.clip(amap, 0.0, 1.0)[:, :, None]
+    def _apply_reverse_alpha(
+        self, image: NDArray[Any], amap: NDArray[Any], region: tuple[int, int, int, int]
+    ) -> NDArray[Any]:
+        """Invert the alpha blend with ``amap``: ``original = (wm - a*logo)/(1-a)``.
+
+        ``amap`` is zero everywhere except the glyph ``region`` (x, y, w, h), so the
+        blend is a no-op (``(wm - 0)/(1 - 0) == wm``) outside it. Compute the math on
+        the glyph crop only and copy the rest through unchanged -- byte-identical to a
+        full-frame pass (a uint8 round-trip through float32 is exact), but O(glyph)
+        instead of O(image): a full-frame pass costs ~275 ms on a 12 MP frame for a
+        glyph that is <0.1% of it, and it runs once per candidate placement.
+        """
+        out = image.copy()
+        x1, y1, gw, gh = region
+        x2, y2 = x1 + gw, y1 + gh
+        if y1 >= y2 or x1 >= x2:
+            return out
+        a3 = np.clip(amap[y1:y2, x1:x2], 0.0, 1.0)[:, :, None]
         logo = np.array(self.config.alpha_logo_bgr, np.float32)
-        return np.clip((image.astype(np.float32) - a3 * logo) / np.clip(1.0 - a3, 0.25, 1.0), 0, 255).astype(np.uint8)
+        roi = out[y1:y2, x1:x2].astype(np.float32)
+        out[y1:y2, x1:x2] = np.clip((roi - a3 * logo) / np.clip(1.0 - a3, 0.25, 1.0), 0, 255).astype(np.uint8)
+        return out
 
     def remove_watermark_reverse_alpha(self, image: NDArray[Any], *, residual_inpaint: bool = True) -> NDArray[Any]:
         """Recover the original pixels by inverting the alpha blend, then clear the
@@ -335,8 +352,8 @@ class TextMarkEngine:
         best_out: NDArray[Any] | None = None
         best_amap: NDArray[Any] | None = None
         best_residual = float("inf")
-        for amap, _region in maps:
-            out = self._apply_reverse_alpha(image, amap)
+        for amap, region in maps:
+            out = self._apply_reverse_alpha(image, amap, region)
             residual = self.detect(out).confidence
             if residual < best_residual:
                 best_residual, best_out, best_amap = residual, out, amap

src/remove_ai_watermarks/cli.py

@@ -13,7 +13,7 @@ import json
 import logging
 import time
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Literal
+from typing import TYPE_CHECKING, Any, Literal, NoReturn
 
 import click
 
@@ -317,6 +317,49 @@ def _remove_visible_auto(
     return result, best.label
 
 
+# Exit code for the standalone ``visible`` command when no visible mark was
+# removed -- distinct from success (0) and a hard error (1) so a wrapping
+# service can tell "nothing to do here" apart and surface guidance instead of
+# re-serving the unchanged input as a finished result.
+EXIT_NO_VISIBLE_MARK = 2
+
+
+def _no_visible_mark_exit(source: Path) -> NoReturn:
+    """Explain why no visible watermark was removed, then exit non-zero.
+
+    The visible registry handles only known visual marks (the Gemini sparkle and
+    the Doubao/Jimeng/Samsung text strips). Most real uploads carry no such mark
+    -- frequently an invisible/metadata watermark instead (e.g. an OpenAI or
+    Gemini image whose only signal is C2PA + SynthID). Returning the input
+    unchanged with exit 0 reads as success to a caller and re-serves the
+    watermarked image -- the recurring "it didn't work" report. Instead, run a
+    cheap metadata-only :func:`identify`, tell the user what the image actually
+    carries and which command removes it, and exit
+    :data:`EXIT_NO_VISIBLE_MARK`.
+    """
+    from remove_ai_watermarks.identify import identify
+
+    report = identify(source, check_visible=False, check_invisible=False)
+    if report.is_ai_generated and report.watermarks:
+        plat = report.platform or "an unidentified platform"
+        console.print(
+            f"  This image carries an invisible/metadata watermark ({plat}), not a visible mark,\n"
+            "  so the 'visible' command cannot remove it. Run the full pipeline instead:\n"
+            f"    remove-ai-watermarks all {source.name}"
+        )
+    else:
+        console.print(
+            "  No visible mark and no readable AI provenance signal. This does not prove\n"
+            "  the image is clean: an invisible pixel watermark such as SynthID cannot be\n"
+            "  detected here once the metadata proxy is absent (it may have been stripped\n"
+            "  earlier). If the image is AI-generated, regenerate the pixels with:\n"
+            f"    remove-ai-watermarks all {source.name}\n"
+            "  If instead there is a logo or object to remove, target it with the region eraser:\n"
+            f"    remove-ai-watermarks erase {source.name} --region x,y,w,h"
+        )
+    raise SystemExit(EXIT_NO_VISIBLE_MARK)
+
+
 def _read_bgr_and_alpha(path: Path) -> tuple[NDArray[Any] | None, NDArray[Any] | None]:
     """Read an image preserving its alpha channel separately.
 
@@ -450,10 +493,9 @@ def cmd_visible(
     if mark == "auto":
         best = registry.best_auto_mark(image)
         if best is None:
-            console.print("  Warning: No known visible mark detected (gemini / doubao).")
+            console.print("  No known visible mark detected (gemini / doubao / jimeng / samsung).")
             if detect:
-                console.print("  Skipping. Use --mark <name> --no-detect to force.")
-                raise SystemExit(0)
+                _no_visible_mark_exit(source)
             target = "gemini"  # forced (no-detect): fall back to the default mark
         else:
             target = best.key
@@ -464,8 +506,8 @@ def cmd_visible(
     chosen = registry.get_mark(target)
     det = chosen.detect(image)
     if detect and not det.detected:
-        console.print(f"  Warning: {chosen.label} not detected  (conf {det.confidence:.2f}). Use --no-detect to force.")
-        raise SystemExit(0)
+        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})")
 

src/remove_ai_watermarks/gemini_engine.py

@@ -142,6 +142,19 @@ class GeminiEngine:
     # gate separates them with a wide margin.
     _OVERSUB_FOOTPRINT_FRAC = 0.05
 
+    # Mid-tone over-subtraction (2026-06-18 prod "the color just changed, not removed"
+    # report). The numerator fraction above only trips when reverse-alpha drives a
+    # footprint pixel fully NEGATIVE -- the dark-background black-pit case. On a MID-TONE
+    # background a sparkle fainter than the captured alpha is over-subtracted into a
+    # visibly DARKER-than-background diamond while no pixel ever crosses zero, so the
+    # numerator gate misses it and ships the dark mark. Predict the reverse-alpha output
+    # at the bright core, (core - a*logo)/(1-a); when it lands more than this many gray
+    # levels BELOW the local background ring, reverse-alpha would leave a dark diamond --
+    # inpaint instead. Calibrated wide: clean removals predict within ~12 of background
+    # (demo_banana ~-1, a bright-bg sparkle ~-12), the prod regression predicts ~-40 and
+    # the issue #30 dark case ~-82, so 25 separates keep-vs-inpaint with margin.
+    _OVERSUB_DARK_MARGIN = 25.0
+
     # Per-image alpha gain (under-subtraction fix). The captured alpha peaks ~0.51
     # (a ~51%-opaque sparkle). Some real Gemini sparkles are rendered MORE opaque,
     # so the fixed alpha under-subtracts and reverse-alpha leaves a bright residual
@@ -642,19 +655,24 @@ class GeminiEngine:
         a_cap = float(alpha_roi.max())
         if a_cap < 0.2:
             return None
-        gray = image.astype(np.float32).mean(axis=2)
         core = alpha_roi >= a_cap * self._ALPHA_GAIN_CORE_FRAC
         if not bool(core.any()):
             return None
-        core_obs = float(np.percentile(gray[y1:y2, x1:x2][core], 75))
-        # Local background = a ring just outside the footprint box.
+        # Convert only the footprint+ring crop to gray, not the whole image: every
+        # sample below lives inside the ring box, so a full-image mean is wasted work
+        # that scales with resolution (~70 ms on a 12 MP image, recomputed for both
+        # the alpha-gain estimate and the over-subtraction gate). The crop is sized by
+        # the footprint, so this is O(footprint^2) regardless of image size.
         ih, iw = image.shape[:2]
         pad = int((x2 - x1) * 0.7)
         ry1, ry2 = max(0, y1 - pad), min(ih, y2 + pad)
         rx1, rx2 = max(0, x1 - pad), min(iw, x2 + pad)
-        ring = gray[ry1:ry2, rx1:rx2]
+        ring = image[ry1:ry2, rx1:rx2].astype(np.float32).mean(axis=2)
+        # Footprint box expressed in ring-crop coordinates.
+        fy1, fy2, fx1, fx2 = y1 - ry1, y2 - ry1, x1 - rx1, x2 - rx1
+        core_obs = float(np.percentile(ring[fy1:fy2, fx1:fx2][core], 75))
         ring_mask = np.ones(ring.shape, dtype=bool)
-        ring_mask[y1 - ry1 : y2 - ry1, x1 - rx1 : x2 - rx1] = False
+        ring_mask[fy1:fy2, fx1:fx2] = False
         if int(ring_mask.sum()) < 10:
             return None
         return core_obs, float(np.median(ring[ring_mask])), a_cap
@@ -704,11 +722,19 @@ class GeminiEngine:
         alpha_map: NDArray[Any],
         position: tuple[int, int],
     ) -> bool:
-        """True when reverse-alpha would drive the footprint dark (issue #30).
+        """True when reverse-alpha would drive the footprint dark.
 
-        Tests the numerator ``watermarked - alpha*logo`` over the sparkle body: a
-        brightening overlay can never make it negative, so a large negative fraction
-        means the fixed alpha over-estimates this image's opacity.
+        Two signatures of the captured alpha over-estimating this image's sparkle
+        opacity, either of which means reverse-alpha would leave a dark mark:
+
+        1. Dark-background black pit (issue #30): the numerator
+           ``watermarked - alpha*logo`` over the sparkle body. A brightening overlay
+           can never make it negative, so a large negative fraction means the fixed
+           alpha over-subtracts past black.
+        2. Mid-tone dark diamond (see ``_OVERSUB_DARK_MARGIN``): on a mid-tone
+           background the over-subtraction darkens the core well below the background
+           without any pixel crossing zero, so case 1 misses it. Predict the
+           reverse-alpha core output and trip when it lands far below the local ring.
         """
         placed = self._footprint_indices(alpha_map, position, image.shape)
         if placed is None:
@@ -720,7 +746,18 @@ class GeminiEngine:
         roi = image[y1:y2, x1:x2].astype(np.float32)
         numerator = roi.mean(axis=2) - np.clip(alpha_roi, 0.0, 0.99) * self.logo_value
         frac = float((numerator[body] < 0).sum()) / float(body.sum())
-        return frac > self._OVERSUB_FOOTPRINT_FRAC
+        if frac > self._OVERSUB_FOOTPRINT_FRAC:
+            return True
+
+        # Mid-tone darkening: predict the reverse-alpha output at the bright core and
+        # compare to the local background ring (reuses the FP-gate / alpha-gain machinery).
+        cb = self._core_and_bg(image, alpha_map, position)
+        if cb is None:
+            return False
+        core_obs, bg, a_cap = cb
+        a = min(a_cap, 0.99)
+        predicted_core = (core_obs - a * self.logo_value) / (1.0 - a)
+        return predicted_core < bg - self._OVERSUB_DARK_MARGIN
 
     def _inpaint_footprint(
         self,
@@ -929,17 +966,21 @@ class GeminiEngine:
         return result
 
 
-def detect_sparkle_confidence(image_path: Path) -> float | None:
+def detect_sparkle_confidence(image_path: Path, *, image: NDArray[Any] | None = None) -> float | None:
     """Visible-sparkle detection confidence for a file, for provenance use.
 
     Loads the image with cv2 and runs :meth:`GeminiEngine.detect_watermark`.
     Returns the NCC confidence in [0, 1], or None if the image cannot be read
     (cv2 returns None for unsupported containers such as HEIC). Kept here so the
     cv2 dependency stays in this module; callers apply their own threshold.
+
+    ``image`` lets a caller that has already decoded the file (e.g. ``identify``
+    running several visible-mark detectors) pass the BGR array to avoid a second
+    full decode; when None the file is read from ``image_path``.
     """
     from remove_ai_watermarks import image_io
 
-    img = image_io.imread(image_path)
+    img = image if image is not None else image_io.imread(image_path)
     if img is None:
         return None
     return float(GeminiEngine().detect_watermark(img).confidence)

src/remove_ai_watermarks/identify.py

@@ -45,6 +45,9 @@ from remove_ai_watermarks.noai.constants import C2PA_AI_TOOLS, C2PA_AI_VENDORS,
 
 if TYPE_CHECKING:
     from pathlib import Path
+    from typing import Any
+
+    from numpy.typing import NDArray
 
     from remove_ai_watermarks.watermark_registry import MarkDetection
 
@@ -358,19 +361,21 @@ def _integrity_clashes(
     return clashes
 
 
-def _visible_sparkle(image_path: Path) -> float | None:
+def _visible_sparkle(image_path: Path, *, image: NDArray[Any] | None = None) -> float | None:
     """Visible Gemini-sparkle confidence in [0, 1], or None if unavailable.
 
     Optional: needs cv2/numpy (no GPU). The cv2 work lives in gemini_engine so
     this module stays dependency-light; returns None if cv2 or the engine
-    assets are missing, or the image can't be read.
+    assets are missing, or the image can't be read. ``image`` is a pre-decoded
+    BGR array shared across the visible-mark detectors (see ``identify``) so the
+    file is not decoded once per detector.
     """
     try:
         from remove_ai_watermarks.gemini_engine import detect_sparkle_confidence
     except Exception as exc:  # cv2/engine assets missing
         log.debug("visible-sparkle detector unavailable: %s", exc)
         return None
-    return detect_sparkle_confidence(image_path)
+    return detect_sparkle_confidence(image_path, image=image)
 
 
 # Visible text marks (registry keys) -> human-readable platform, mirroring the
@@ -384,14 +389,16 @@ _VISIBLE_MARK_PLATFORM = {
 }
 
 
-def _visible_text_marks(image_path: Path) -> list[MarkDetection]:
+def _visible_text_marks(image_path: Path, *, image: NDArray[Any] | None = None) -> list[MarkDetection]:
     """Detected visible Doubao/Jimeng marks (registry ``MarkDetection`` list).
 
     The Gemini sparkle keeps its own ``_visible_sparkle`` path (file-level
     confidence); these two text marks reuse the registry detectors, which apply
     each engine's calibrated NCC threshold via ``MarkDetection.detected``.
     Optional: needs cv2/numpy; returns ``[]`` if the engines/assets are missing
-    or the image can't be read.
+    or the image can't be read. ``image`` is a pre-decoded BGR array shared
+    across the visible-mark detectors (see ``identify``) so the file is not
+    decoded once per detector.
     """
     try:
         from remove_ai_watermarks.image_io import imread
@@ -399,7 +406,8 @@ def _visible_text_marks(image_path: Path) -> list[MarkDetection]:
     except Exception as exc:  # cv2/engine assets missing
         log.debug("visible-mark detectors unavailable: %s", exc)
         return []
-    image = imread(image_path)
+    if image is None:
+        image = imread(image_path)
     if image is None:
         return []
     detections: list[MarkDetection] = []
@@ -669,17 +677,32 @@ def identify(image_path: Path, *, check_visible: bool = True, check_invisible: b
         or xai_sig
     )
 
+    # Decode the file ONCE for every visible-mark detector. The sparkle and the
+    # text-mark detectors both consume a BGR array; letting each re-read the file
+    # was two full cv2 decodes of the same bitmap, which spikes memory on a small
+    # worker. None (cv2 missing / unreadable container) makes each detector fall
+    # back to its own read, preserving the old behavior.
+    vis_image: NDArray[Any] | None = None
+    if check_visible:
+        try:
+            from remove_ai_watermarks.image_io import imread
+
+            vis_image = imread(image_path)
+        except Exception as exc:  # cv2 missing - detectors fall back / no-op
+            log.debug("visible-mark decode unavailable: %s", exc)
+
     # ── Visible Gemini sparkle (fallback for stripped-metadata case) ─
-    if check_visible and (conf := _visible_sparkle(image_path)) is not None and conf >= _SPARKLE_THRESHOLD:
-        signals.append(Signal("visible_sparkle", f"NCC confidence {conf:.2f}", "medium"))
-        watermarks.append(f"Visible Gemini sparkle (confidence {conf:.2f})")
+    sparkle_conf = _visible_sparkle(image_path, image=vis_image) if check_visible else None
+    if sparkle_conf is not None and sparkle_conf >= _SPARKLE_THRESHOLD:
+        signals.append(Signal("visible_sparkle", f"NCC confidence {sparkle_conf:.2f}", "medium"))
+        watermarks.append(f"Visible Gemini sparkle (confidence {sparkle_conf:.2f})")
         if platform is None:
             platform = "Google Gemini family (visible sparkle detected)"
 
     # ── Visible Doubao / Jimeng text marks (registry; same stripped-metadata
     #    fallback role as the Gemini sparkle above) ─
     if check_visible:
-        for det in _visible_text_marks(image_path):
+        for det in _visible_text_marks(image_path, image=vis_image):
             signals.append(Signal(f"visible_{det.key}", f"NCC confidence {det.confidence:.2f}", "medium"))
             watermarks.append(f"Visible {det.label} (confidence {det.confidence:.2f})")
             if platform is None:

src/remove_ai_watermarks/invisible_engine.py

@@ -269,7 +269,11 @@ class InvisibleEngine:
             # each stage independently imread/imwrote the full-res output, so a run
             # with several stages PNG-decoded+re-encoded the same image 2-4 times.
             # PNG is lossless, so the single-write output is byte-identical.
-            needs_restore = target is not None  # the input was resized before diffusion
+            # Diffusers rounds native dimensions down to the latent grid (multiples
+            # of 8), even when our own resolution policy did not resize the input.
+            # Route those outputs through the same final resize so --no-polish does
+            # not silently change e.g. 1448x1086 into 1448x1080.
+            needs_restore = target is not None or any(dimension % 8 for dimension in orig_size)
             if humanize > 0.0 or unsharp > 0.0 or adaptive_polish or needs_restore:
                 import cv2
 

src/remove_ai_watermarks/metadata.py

@@ -297,17 +297,13 @@ def has_ai_metadata(image_path: Path) -> bool:
     except Exception as exc:
         logger.debug("PIL could not open %s for metadata scan: %s", image_path, exc)
 
-    # Check C2PA — via the official ``c2pa`` lib if available, otherwise via a
-    # binary scan that also catches AVIF/HEIF/JPEG-XL containers (PIL doesn't
-    # expose their metadata uniformly).
-    try:
-        # optional official lib, not a declared dep -> falls back to the binary scan
-        from c2pa import has_c2pa_metadata  # pyright: ignore[reportMissingImports, reportUnknownVariableType]
+    # Check C2PA — via the official c2pa-python reader first (spec-tracking, every
+    # container it supports), then a binary scan that also catches AVIF/HEIF/JPEG-XL
+    # containers and synthetic/partial blobs the validator rejects.
+    from remove_ai_watermarks.noai.c2pa import read_manifest_store_json
 
-        if has_c2pa_metadata(image_path):
-            return True
-    except ImportError:
-        pass
+    if read_manifest_store_json(image_path) is not None:
+        return True
 
     # Binary scan covers C2PA (PNG caBX, JPEG APP11, AVIF/HEIF/JXL uuid boxes)
     # and IPTC AI markers in XMP. First 512KB (plus late ISOBMFF provenance boxes).

src/remove_ai_watermarks/noai/c2pa.py

@@ -1,8 +1,11 @@
 """C2PA (Coalition for Content Provenance and Authenticity) metadata handling.
 
-C2PA metadata is embedded in PNG files as a JUMBF container chunk
-(``caBX``).  This module can detect, extract, and re-inject those
-chunks.  Supported issuers:
+Reading goes through the official c2pa-python ``Reader`` first (any container it
+supports), via ``extract_c2pa_info`` / ``read_manifest_store_json``. The
+hand-rolled PNG ``caBX`` JUMBF-chunk tools below (``has_c2pa_metadata`` /
+``extract_c2pa_chunk`` / ``inject_c2pa_chunk`` and the ``_extract_c2pa_info_png``
+fallback) cover raw-chunk extraction, re-injection, and the cases the validator
+rejects (synthetic/partial blobs, a broken/absent wheel). Known issuers:
 
 - Google Imagen
 - Adobe Firefly
@@ -10,17 +13,22 @@ chunks.  Supported issuers:
 - OpenAI (ChatGPT, GPT-4o, Sora, DALL-E)
 - Truepic (signing authority)
 
-The parser uses byte-level scanning — it does not validate JUMBF/CBOR
-structure but reliably identifies known signatures, issuers, tools,
-and actions.
+The fallback parser uses byte-level scanning — it does not validate JUMBF/CBOR
+structure but reliably identifies known signatures, issuers, tools, and actions.
+The vendor / source-type / SynthID / soft-binding registry scan
+(``_populate_registry_fields``) is shared by both the reader and fallback paths.
 """
 
 from __future__ import annotations
 
+import contextlib
+import functools
+import json
+import logging
 import re
 import struct
 from pathlib import Path
-from typing import Any
+from typing import Any, cast
 
 from remove_ai_watermarks.noai.constants import (
     C2PA_ACTIONS,
@@ -33,6 +41,72 @@ from remove_ai_watermarks.noai.constants import (
     SYNTHID_C2PA_ISSUERS,
 )
 
+log = logging.getLogger(__name__)
+
+# Official C2PA reader (c2pa-python, a core dependency). It is the primary,
+# spec-tracking manifest parser; the hand-rolled caBX/CBOR scanner below stays as
+# a fallback for synthetic/partial blobs the validator rejects. The import is
+# guarded so a partially-broken install degrades to the byte-scan rather than
+# crashing the dependency-light identify path.
+_C2paReader: Any = None
+with contextlib.suppress(Exception):  # broken/absent wheel -> byte-scan fallback
+    from c2pa import Reader as _C2paReader  # pyright: ignore[reportMissingTypeStubs]
+_C2PA_READER_AVAILABLE = _C2paReader is not None
+
+
+def reader_available() -> bool:
+    """True when the official c2pa-python Reader imported successfully."""
+    return _C2PA_READER_AVAILABLE
+
+
+def read_manifest_store_json(image_path: Path) -> str | None:
+    """Return the full C2PA manifest-store JSON for ``image_path``, or None.
+
+    Uses the official c2pa-python ``Reader`` (any container it supports: PNG,
+    JPEG, WebP, AVIF/HEIF, MP4, ...). Returns None when the reader is unavailable,
+    the file carries no parseable manifest, or parsing fails. The JSON is the
+    WHOLE store (every manifest plus ingredient manifests), matching the
+    whole-chunk semantics of the legacy byte scan -- an AI-source marker in a
+    parent/ingredient manifest (e.g. a ChatGPT edit of a Sora generation) is
+    still seen.
+
+    Memoized per (path, mtime): one identify/get_ai_metadata call invokes the
+    structured parser ~3 times on the same file, so the cache turns the repeated
+    crypto-validating reads into one.
+    """
+    if not _C2PA_READER_AVAILABLE:
+        return None
+    try:
+        mtime = image_path.stat().st_mtime_ns
+    except OSError:
+        return _read_manifest_store_impl(str(image_path))
+    return _read_manifest_store_cached(str(image_path), mtime)
+
+
+@functools.lru_cache(maxsize=8)
+def _read_manifest_store_cached(path_str: str, _mtime_ns: int) -> str | None:
+    """Cache shim: ``_mtime_ns`` is part of the key only (invalidates on change)."""
+    return _read_manifest_store_impl(path_str)
+
+
+def _read_manifest_store_impl(path_str: str) -> str | None:
+    # try_create returns None when there is no manifest; a default Reader does no
+    # trust enforcement, so an untrusted signer still yields the manifest content
+    # (we report what is in the file, we do not gate on certificate trust).
+    try:
+        reader = _C2paReader.try_create(path_str)
+    except Exception as exc:  # malformed manifest, unsupported container, etc.
+        log.debug("c2pa Reader could not parse %s: %s", path_str, exc)
+        return None
+    if reader is None:
+        return None
+    try:
+        with reader:
+            return reader.json()
+    except Exception as exc:  # pragma: no cover - reader opened but json() failed
+        log.debug("c2pa Reader.json() failed on %s: %s", path_str, exc)
+        return None
+
 
 def has_c2pa_metadata(image_path: Path) -> bool:
     """
@@ -90,16 +164,83 @@ def has_c2pa_metadata(image_path: Path) -> bool:
     return False
 
 
+def _claim_generator_from_store(store: dict[str, Any]) -> str | None:
+    """Structured claim-generator name from the active manifest of a store dict.
+
+    Prefers the top-level ``claim_generator`` string (Firefly: "Adobe_Firefly"),
+    falling back to the first ``claim_generator_info[].name`` (ChatGPT keys it
+    only there). isprintable() guards against odd binary-ish values.
+    """
+    active = _active_manifest(store)
+    generator: Any = active.get("claim_generator")
+    if not (isinstance(generator, str) and generator):
+        info_list: list[Any] = active.get("claim_generator_info") or []
+        if info_list and isinstance(first := info_list[0], dict):
+            generator = cast("dict[str, Any]", first).get("name")
+    return generator if isinstance(generator, str) and generator and generator.isprintable() else None
+
+
+def _active_manifest(store: dict[str, Any]) -> dict[str, Any]:
+    """The active manifest dict from a manifest-store dict, or {} when absent."""
+    manifests: Any = store.get("manifests")
+    if not isinstance(manifests, dict):
+        return {}
+    active = cast("dict[str, Any]", manifests).get(store.get("active_manifest", ""))
+    return cast("dict[str, Any]", active) if isinstance(active, dict) else {}
+
+
+def _info_from_store_json(store_json: str) -> dict[str, Any]:
+    """Build the C2PA info dict from a c2pa-python manifest-store JSON string."""
+    store_bytes = store_json.encode("utf-8")
+    c2pa_info: dict[str, Any] = {
+        "has_c2pa": True,
+        "type": "C2PA (Coalition for Content Provenance and Authenticity)",
+        "c2pa_manifest": f"C2PA manifest store ({len(store_bytes)} bytes)",
+    }
+    # The whole-store JSON carries every vendor / source-type / SynthID /
+    # soft-binding signature (across active + ingredient manifests), so the same
+    # registry scan that runs on the raw caBX chunk applies unchanged here.
+    _populate_registry_fields(store_bytes, c2pa_info)
+
+    try:
+        parsed: Any = json.loads(store_json)
+    except (ValueError, TypeError):
+        return c2pa_info
+    if not isinstance(parsed, dict):
+        return c2pa_info
+    store = cast("dict[str, Any]", parsed)
+    if generator := _claim_generator_from_store(store):
+        c2pa_info["claim_generator"] = generator
+    sig: Any = _active_manifest(store).get("signature_info")
+    if isinstance(sig, dict) and (time := cast("dict[str, Any]", sig).get("time")):
+        c2pa_info["timestamp"] = str(time)
+    return c2pa_info
+
+
 def extract_c2pa_info(image_path: Path) -> dict[str, Any]:
     """
-    Extract basic C2PA metadata information from an image.
+    Extract C2PA metadata information from an image.
+
+    Uses the official c2pa-python reader first (any supported container), falling
+    back to the hand-rolled PNG caBX parser when the reader is unavailable or the
+    file carries no parseable manifest (synthetic/partial blobs).
 
     Args:
         image_path: Path to the image file.
 
     Returns:
-        Dictionary containing C2PA metadata info.
+        Dictionary containing C2PA metadata info, or {} when none is found.
     """
+    image_path = Path(image_path)
+
+    if (store_json := read_manifest_store_json(image_path)) is not None:
+        return _info_from_store_json(store_json)
+
+    return _extract_c2pa_info_png(image_path)
+
+
+def _extract_c2pa_info_png(image_path: Path) -> dict[str, Any]:
+    """Fallback PNG caBX parser, used when the c2pa-python reader finds nothing."""
     c2pa_info: dict[str, Any] = {}
 
     if not has_c2pa_metadata(image_path):
@@ -199,25 +340,66 @@ def soft_binding_vendors_in(buffer: bytes) -> list[str]:
     return sorted({name for sig, name in C2PA_SOFT_BINDINGS.items() if sig in buffer})
 
 
+def _populate_registry_fields(buf: bytes, c2pa_info: dict[str, Any]) -> bool:
+    """Populate the registry-driven C2PA fields by scanning ``buf``.
+
+    Shared by the legacy caBX-chunk parser and the c2pa-python store-JSON path so
+    both produce an identical dict shape. ``buf`` is the raw manifest bytes for
+    the former and the manifest-store JSON (UTF-8) for the latter; the vendor /
+    tool / action / source-type / SynthID / soft-binding signatures appear in
+    both. Sets ``issuer``, ``ai_tool``, ``actions``, ``source_type``,
+    ``synthid_vendors`` / ``synthid_watermark``, ``soft_binding_vendors`` /
+    ``soft_binding`` when present and returns whether the source type is AI.
+    """
+    if issuers := [name for sig, name in C2PA_ISSUERS.items() if sig in buf]:
+        c2pa_info["issuer"] = ", ".join(dict.fromkeys(issuers))
+
+    if ai_tools := [name for sig, name in C2PA_AI_TOOLS.items() if sig in buf]:
+        c2pa_info["ai_tool"] = ", ".join(dict.fromkeys(ai_tools))
+
+    if actions := [name for sig, name in C2PA_ACTIONS.items() if sig in buf]:
+        c2pa_info["actions"] = ", ".join(actions)
+
+    # Digital source type (matched anywhere in the store, including ingredient
+    # manifests -- a ChatGPT edit of a Sora generation carries the AI marker on
+    # the parent, not the active manifest).
+    ai_source = False
+    if b"trainedAlgorithmicMedia" in buf:
+        c2pa_info["source_type"] = "trainedAlgorithmicMedia (AI-generated)"
+        ai_source = True
+    elif b"algorithmicMedia" in buf:
+        c2pa_info["source_type"] = "algorithmicMedia"
+    elif b"compositeWithTrainedAlgorithmicMedia" in buf:
+        c2pa_info["source_type"] = "compositeWithTrainedAlgorithmicMedia (AI-enhanced)"
+        ai_source = True
+
+    # SynthID pixel-watermark proxy: a C2PA manifest from a SynthID-using
+    # vendor (Google/OpenAI) on AI-generated content implies an invisible
+    # SynthID watermark in the pixels (see SYNTHID_C2PA_ISSUERS).
+    synthid_vendors = synthid_vendors_in(buf)
+    if synthid_vendors and ai_source:
+        c2pa_info["synthid_vendors"] = synthid_vendors
+        c2pa_info["synthid_watermark"] = synthid_verdict(", ".join(synthid_vendors))
+
+    # Soft-binding: a forensic/third-party watermark vendor named in the
+    # manifest (Adobe TrustMark, Digimarc, ...), independent of the issuer.
+    soft_binding_vendors = soft_binding_vendors_in(buf)
+    if soft_binding_vendors:
+        c2pa_info["soft_binding_vendors"] = soft_binding_vendors
+        c2pa_info["soft_binding"] = ", ".join(soft_binding_vendors)
+
+    return ai_source
+
+
 def _parse_c2pa_chunk(chunk_data: bytes, c2pa_info: dict[str, Any]) -> None:
-    """Parse C2PA chunk data and populate info dictionary."""
+    """Parse a raw caBX chunk payload and populate the info dictionary.
+
+    The fallback path, used when the official c2pa-python reader is unavailable
+    or rejects the file (synthetic/partial blobs, broken installs).
+    """
     c2pa_info["c2pa_manifest"] = f"C2PA manifest ({len(chunk_data)} bytes)"
 
-    # Find issuers
-    issuers: list[str] = []
-    for sig, name in C2PA_ISSUERS.items():
-        if sig in chunk_data:
-            issuers.append(name)
-    if issuers:
-        c2pa_info["issuer"] = ", ".join(set(issuers))
-
-    # Find AI tools
-    ai_tools: list[str] = []
-    for sig, name in C2PA_AI_TOOLS.items():
-        if sig in chunk_data:
-            ai_tools.append(name)
-    if ai_tools:
-        c2pa_info["ai_tool"] = ", ".join(set(ai_tools))
+    _populate_registry_fields(chunk_data, c2pa_info)
 
     # Claim generator and spec version: read the CBOR text-string values
     # directly (regex byte-grabbing produced artifacts like ``fGPT-4o``).
@@ -229,14 +411,6 @@ def _parse_c2pa_chunk(chunk_data: bytes, c2pa_info: dict[str, Any]) -> None:
     if (spec := cbor_text_after(chunk_data, b"specVersion")) and spec.isprintable():
         c2pa_info["c2pa_spec"] = spec
 
-    # Find actions
-    actions: list[str] = []
-    for sig, name in C2PA_ACTIONS.items():
-        if sig in chunk_data:
-            actions.append(name)
-    if actions:
-        c2pa_info["actions"] = ", ".join(actions)
-
     # Find timestamps
     timestamp_matches = re.findall(rb"(\d{14}Z)", chunk_data)
     if timestamp_matches:
@@ -244,32 +418,6 @@ def _parse_c2pa_chunk(chunk_data: bytes, c2pa_info: dict[str, Any]) -> None:
         if len(timestamp_matches) > 1:
             c2pa_info["timestamps"] = [t.decode("utf-8") for t in timestamp_matches[:3]]
 
-    # Find digital source type
-    ai_source = False
-    if b"trainedAlgorithmicMedia" in chunk_data:
-        c2pa_info["source_type"] = "trainedAlgorithmicMedia (AI-generated)"
-        ai_source = True
-    elif b"algorithmicMedia" in chunk_data:
-        c2pa_info["source_type"] = "algorithmicMedia"
-    elif b"compositeWithTrainedAlgorithmicMedia" in chunk_data:
-        c2pa_info["source_type"] = "compositeWithTrainedAlgorithmicMedia (AI-enhanced)"
-        ai_source = True
-
-    # SynthID pixel-watermark proxy: a C2PA manifest from a SynthID-using
-    # vendor (Google/OpenAI) on AI-generated content implies an invisible
-    # SynthID watermark in the pixels (see SYNTHID_C2PA_ISSUERS).
-    synthid_vendors = synthid_vendors_in(chunk_data)
-    if synthid_vendors and ai_source:
-        c2pa_info["synthid_vendors"] = synthid_vendors
-        c2pa_info["synthid_watermark"] = synthid_verdict(", ".join(synthid_vendors))
-
-    # Soft-binding: a forensic/third-party watermark vendor named in the
-    # manifest (Adobe TrustMark, Digimarc, ...), independent of the issuer.
-    soft_binding_vendors = soft_binding_vendors_in(chunk_data)
-    if soft_binding_vendors:
-        c2pa_info["soft_binding_vendors"] = soft_binding_vendors
-        c2pa_info["soft_binding"] = ", ".join(soft_binding_vendors)
-
 
 def extract_c2pa_chunk(image_path: Path) -> bytes | None:
     """

src/remove_ai_watermarks/noai/watermark_profiles.py

@@ -37,12 +37,22 @@ CONTROLNET_CANNY_MODEL = "xinsir/controlnet-canny-sdxl-1.0"
 # applies to BOTH pipelines (`sdxl` plain img2img and `controlnet`) -- see "why one
 # ladder" below.
 #
-# Data basis (see docs/synthid.md sections 2.2 / 5.5): the values are the ORACLE-
-# CERTIFIED controlnet floors (2026-06-04, isolated Modal cert app, each vendor on its
-# own verifier): OpenAI 0.20 (2 photoreal x 3 seeds = 6/6 clean, resolution-independent),
-# Google 0.30 (clean on 2/2 seeds, validated ONLY at <= 1536 -- Gemini is resolution-
-# sensitive, native ~2816 likely needs ~0.35+). Unknown vendor gets the Google (more
-# robust watermark) value: safe-by-default.
+# Data basis (see docs/synthid.md sections 2.2 / 5.5): ORACLE-CERTIFIED controlnet floors.
+# A 2026-06-14 re-test on the deployed Modal worker (the production controlnet pipeline)
+# LOWERED the ladder back to OpenAI 0.10 / Google 0.15: each output verified on its own
+# oracle (openai.com/verify for OpenAI, the Google Gemini app for Google), all clean ->
+#   - OpenAI 0.10: 2 photoreal images (1402 / 1448 px), SynthID not found on either.
+#   - Google 0.15: 2 NATIVE-resolution images (both 2816x1536), SynthID not found on
+#     either -- this directly retires the earlier "native ~2816 likely needs ~0.35+"
+#     guess, which was speculative and never oracle-checked at that resolution.
+# This supersedes the 2026-06-04 cert (OpenAI 0.20 / Google 0.30), whose higher floor a
+# pixel-fidelity sweep showed was ~2x the removal floor and over-regenerated for no
+# efficacy gain (Google MAE -20% at 0.15 vs 0.30, no SynthID returning). Unknown vendor
+# tracks the Google (more robust watermark) value -> 0.15, still safe-by-default and the
+# floor that real (no-vendor) photos hit, so it also minimizes damage when there is in
+# fact nothing to remove. CAVEAT: the re-test is n=2 per vendor on photoreal / landscape
+# content; FLAT-GRAPHIC hard cases (the historical `sdxl` weak spot) were NOT in the
+# sample, so if an oracle still reads SynthID on a flat output, raise `--strength`.
 #
 # Why ONE ladder for both pipelines (2026-06-09): the certification was run on
 # controlnet, and it does NOT transfer to `sdxl` by symmetry -- the two pipelines have
@@ -57,9 +67,9 @@ CONTROLNET_CANNY_MODEL = "xinsir/controlnet-canny-sdxl-1.0"
 # this is a MARGIN argument for `sdxl`, not a fresh certification -- there is no local
 # SynthID detector, so if an oracle still reads SynthID on a flat `sdxl` output, raise
 # `--strength`.
-OPENAI_STRENGTH = 0.20
-GEMINI_STRENGTH = 0.30
-UNKNOWN_STRENGTH = 0.30
+OPENAI_STRENGTH = 0.10
+GEMINI_STRENGTH = 0.15
+UNKNOWN_STRENGTH = 0.15
 # Backwards-compatible alias: the vendor-unknown value (what a caller gets without a
 # detected vendor). Kept as DEFAULT_STRENGTH for existing references.
 DEFAULT_STRENGTH = UNKNOWN_STRENGTH

src/remove_ai_watermarks/noai/watermark_remover.py

@@ -757,9 +757,9 @@ def remove_watermark(
 ) -> Path:
     """Convenience function to remove watermark from an image.
 
-    ``strength=None`` lets the profile pick its vendor-adaptive SDXL default
-    (0.10 OpenAI / 0.15 Google / 0.15 unknown, from the C2PA SynthID proxy on the
-    input). Pass a value to override.
+    ``strength=None`` lets the profile pick its vendor-adaptive default
+    (0.20 OpenAI / 0.30 Google / 0.30 unknown, from the C2PA SynthID proxy on the
+    input; same ladder for the controlnet and sdxl pipelines). Pass a value to override.
     """
     from remove_ai_watermarks.noai.watermark_profiles import vendor_for_strength
 

tests/test_cli.py

@@ -125,6 +125,37 @@ class TestVisibleCommand:
         assert "visible AI watermark" in result.output
         assert "--mark" in result.output
 
+    def test_visible_auto_no_mark_exits_two_with_eraser_hint(self, runner, sample_png, tmp_path):
+        # No known visible mark and no AI provenance signal: the command must not
+        # re-serve the input as a finished result. It exits EXIT_NO_VISIBLE_MARK
+        # (2) -- distinct from success (0) and a hard error (1) -- writes no
+        # output file, and points the user at the region eraser.
+        output = tmp_path / "clean.png"
+        result = runner.invoke(main, ["visible", str(sample_png), "-o", str(output)])
+        assert result.exit_code == 2, result.output
+        assert not output.exists()
+        assert "erase" in result.output
+        # The "no signal" branch must NOT imply the image is clean: a missing
+        # metadata proxy is not proof an invisible pixel watermark (SynthID) is
+        # absent, so the message preserves that uncertainty and routes to 'all'.
+        assert "SynthID" in result.output
+        assert "all" in result.output
+
+    def test_visible_auto_no_mark_routes_to_all_when_metadata(self, runner, tmp_path):
+        # An image whose only signal is an invisible/metadata watermark (here SD
+        # generation parameters) has no visible mark to remove; the command must
+        # exit 2 and upsell the full 'all' pipeline rather than the eraser.
+        img = Image.fromarray(np.random.default_rng(0).integers(0, 255, (200, 200, 3), dtype=np.uint8))
+        pnginfo = PngInfo()
+        pnginfo.add_text("parameters", "Steps: 20, Sampler: Euler, a test landscape")
+        src = tmp_path / "ai.png"
+        img.save(src, pnginfo=pnginfo)
+        output = tmp_path / "clean.png"
+        result = runner.invoke(main, ["visible", str(src), "-o", str(output)])
+        assert result.exit_code == 2, result.output
+        assert not output.exists()
+        assert "all" in result.output
+
     def test_visible_basic(self, runner, sample_png, tmp_path):
         output = tmp_path / "clean.png"
         result = runner.invoke(

tests/test_gemini_engine.py

@@ -298,6 +298,28 @@ class TestOverSubtractionGuard:
         dalpha = self.engine.get_interpolated_alpha(dpos[2])
         assert self.engine._reverse_alpha_oversubtracts(dark, dalpha, (dpos[0], dpos[1])) is True
 
+    def test_midtone_background_does_not_leave_dark_diamond(self):
+        """2026-06-18 prod report: a faint sparkle on a MID-TONE background was
+        over-subtracted into a darker-than-background diamond ("the color just
+        changed, not removed"). No footprint pixel crosses zero there, so the
+        numerator gate misses it -- the dark-margin gate must catch it and inpaint.
+        """
+        image, (x, y, w, h) = self._composite_sparkle(bg_value=160)
+        footprint = image[y : y + h, x : x + w]
+        # The numerator (black-pit) gate alone does NOT fire on a mid-tone background.
+        alpha = self.engine.get_interpolated_alpha(w)
+        roi = footprint.astype(np.float32).mean(axis=2)
+        body = alpha[:h, :w] >= self.engine._FOOTPRINT_ALPHA
+        numerator = roi - np.clip(alpha[:h, :w], 0.0, 0.99) * self.engine.logo_value
+        assert float((numerator[body] < 0).sum()) / float(body.sum()) <= self.engine._OVERSUB_FOOTPRINT_FRAC
+        # ...but the over-subtraction guard still trips (via the dark-margin path) and
+        # removal leaves the footprint reading like the mid-tone background, not darker.
+        assert self.engine._reverse_alpha_oversubtracts(image, alpha, (x, y)) is True
+        out = self.engine.remove_watermark(image)
+        cleaned = out[y : y + h, x : x + w]
+        assert abs(float(cleaned.mean()) - 160.0) < 15.0, f"dark diamond: mean={cleaned.mean()}"
+        assert int(cleaned.min()) > 160 - 30, f"dark pit: min={cleaned.min()}"
+
 
 class TestUnderSubtractionGain:
     """Under-subtraction fix: a sparkle MORE opaque than the captured alpha must not

tests/test_identify.py

@@ -434,6 +434,18 @@ class TestIdentifyVisibleTextMarks:
             r = identify(tmp_png_with_ai_metadata, check_visible=True)
         assert r.confidence == "high"
 
+    def test_visible_path_decodes_file_once(self, tmp_clean_png: Path):
+        """The web path identify(check_visible=True, check_invisible=False) must
+        decode the image exactly once and share the array across the sparkle +
+        text-mark detectors. Two decodes of the same bitmap spiked memory on the
+        small web worker (the OOM the decode-once refactor addresses)."""
+        import remove_ai_watermarks.image_io as image_io
+
+        real_imread = image_io.imread
+        with patch.object(image_io, "imread", side_effect=real_imread) as mock_imread:
+            identify(tmp_clean_png, check_visible=True, check_invisible=False)
+        assert mock_imread.call_count == 1
+
 
 # ── Caveats and serialization ───────────────────────────────────────
 

tests/test_invisible_engine.py

@@ -2,6 +2,10 @@
 
 from __future__ import annotations
 
+from types import SimpleNamespace
+
+from PIL import Image
+
 from remove_ai_watermarks.invisible_engine import InvisibleEngine, _target_size, is_available
 
 
@@ -31,6 +35,29 @@ class TestInvisibleEngineInit:
         assert InvisibleEngine.DEFAULT_MODEL_ID == "stabilityai/stable-diffusion-xl-base-1.0"
 
 
+class TestNativeOutputSize:
+    """Model-side latent-grid rounding must not change the public output size."""
+
+    def test_no_polish_restores_native_non_multiple_of_eight_size(self, tmp_path):
+        engine = object.__new__(InvisibleEngine)
+
+        def _remove_watermark(image_path, output_path=None, **_kwargs):
+            out = output_path or image_path.with_stem(image_path.stem + "_clean")
+            # Model-side latent-grid rounding: 18px becomes 16px.
+            Image.open(image_path).crop((0, 0, 24, 16)).save(out)
+            return out
+
+        engine._remover = SimpleNamespace(remove_watermark=_remove_watermark)
+        engine._progress_callback = None
+        src = tmp_path / "src.png"
+        out = tmp_path / "out.png"
+        Image.new("RGB", (24, 18), (128, 128, 128)).save(src)
+
+        engine.remove_watermark(src, out, min_resolution=0, adaptive_polish=False)
+
+        assert Image.open(out).size == (24, 18)
+
+
 class TestTargetSize:
     """Regression guard for the native-resolution decision (issues #10 / #15).
 

tests/test_noai.py

@@ -182,6 +182,29 @@ class TestC2PARealSamples:
         assert has_c2pa_metadata(out)
         assert "OpenAI" in extract_c2pa_info(out)["issuer"]
 
+    def test_extract_info_uses_reader_store(self):
+        """The c2pa-python reader path: structured (not heuristic) extraction."""
+        from remove_ai_watermarks.noai import c2pa
+
+        assert c2pa.reader_available()
+        info = extract_c2pa_info(SAMPLES_DIR / "chatgpt-1.png")
+        # The store-JSON label proves the reader path served this, not the
+        # caBX-chunk fallback ("C2PA manifest (...)").
+        assert info["c2pa_manifest"].startswith("C2PA manifest store")
+        # Structured claim generator is exact, not a CBOR-scanned best-effort.
+        assert info["claim_generator"] == "ChatGPT"
+
+    def test_fallback_to_png_parser_when_reader_unavailable(self, monkeypatch):
+        """With the reader disabled, the hand-rolled PNG parser still works."""
+        from remove_ai_watermarks.noai import c2pa
+
+        monkeypatch.setattr(c2pa, "_C2PA_READER_AVAILABLE", False)
+        info = extract_c2pa_info(SAMPLES_DIR / "chatgpt-1.png")
+        assert info["c2pa_manifest"].startswith("C2PA manifest (")  # chunk path
+        assert "OpenAI" in info["issuer"]
+        assert "trainedAlgorithmicMedia" in info["source_type"]
+        assert "synthid_watermark" in info
+
 
 class TestC2PAInjectValidation:
     def test_inject_rejects_non_png(self, tmp_path):

tests/test_platform.py

@@ -140,11 +140,12 @@ class TestResolveStrength:
         assert resolve_strength(None, "adobe") == UNKNOWN_STRENGTH
 
     def test_ladder_is_the_certified_controlnet_floors(self):
-        # The unified ladder == the oracle-certified controlnet floors (OpenAI 0.20,
-        # Google/unknown 0.30); Google is the more-robust watermark, so it is higher.
-        assert OPENAI_STRENGTH == 0.20
-        assert GEMINI_STRENGTH == 0.30
-        assert UNKNOWN_STRENGTH == 0.30
+        # The unified ladder == the oracle-certified controlnet floors. Lowered on the
+        # 2026-06-14 Modal re-test (OpenAI 0.10, Google/unknown 0.15); Google is the
+        # more-robust watermark, so it is higher.
+        assert OPENAI_STRENGTH == 0.10
+        assert GEMINI_STRENGTH == 0.15
+        assert UNKNOWN_STRENGTH == 0.15
         assert OPENAI_STRENGTH < GEMINI_STRENGTH
 
     def test_default_strength_alias_is_unknown_vendor_value(self):

uv.lock

@@ -15,7 +15,7 @@ resolution-markers = [
 
 [[package]]
 name = "accelerate"
-version = "1.13.0"
+version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -26,9 +26,9 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/8d/75/94cd5d389649578aca399e5aa822637eec18319a1dadc400ffe2f9a7493f/accelerate-1.14.0.tar.gz", hash = "sha256:41b9c4377a54e0b460a959b0defa1b736e4ca0a2373252d9a539964c2afe3c8d", size = 412167, upload-time = "2026-06-11T13:45:52.326Z" }
 wheels = [
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 ]
 
+[[package]]
+name = "c2pa-python"
+version = "0.35.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+    { name = "pytest" },
+    { name = "requests" },
+    { name = "setuptools" },
+    { name = "toml" },
+    { name = "wheel" },
+]
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+    { url = "https://files.pythonhosted.org/packages/82/b5/15ca758e19f76e603581e4e134fc4d1b8aa62427e471915ef475902d054d/c2pa_python-0.35.1-py3-none-win_amd64.whl", hash = "sha256:c3fd0e7d336782ca049a054a9fc5d7677420ecd78c5a754a3af4d20ede562ac4", size = 89555694, upload-time = "2026-06-17T20:46:09.457Z" },
+    { url = "https://files.pythonhosted.org/packages/43/76/810ea55bcec87ffa8df2c1c2c208d03afb88bcab555407b729cd9255dde2/c2pa_python-0.35.1-py3-none-win_arm64.whl", hash = "sha256:47fc05761ed8aee97d0ce811755c6d32bf428917437ec6690b4df3300d33f591", size = 87090751, upload-time = "2026-06-17T20:46:13.415Z" },
+]
+
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 name = "certifi"
 version = "2026.5.20"
@@ -255,6 +278,88 @@ wheels = [
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