chore(release): v0.11.4

docs: dwtDct detector is carrier-fragile (all-ones = artifact), FLUX open-mark unresolvable
Final characterization after a positive-control sweep. The imwatermark dwtDct round-trip fails (28-39/48, below the 44 gate) not on "high texture" as a prior note claimed, but on a broad carrier class: the FLUX fox, doubao, a minimalist-FLAT FLUX generation, AND a clean synthetic bright-flat fill with NO watermark all fail identically. The degenerate all-ones decode is therefore a CARRIER ARTIFACT, not a watermark (the no-watermark synthetic image reproduces it; a double-embed test shows no interference). detect_invisible_watermark is positive-only: trust a hit, treat a None as inconclusive unless a same-carrier positive control first recovers >=44. Consequence: whether BFL hosted FLUX embeds the open DWT-DCT is unresolvable with this detector on the available carriers (textured AND flat FLUX both fail the control). C2PA stays the reliable FLUX signal. Low priority to chase further. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-10-16 22:40:45 +02:00 · 2026-06-19 10:06:04 -07:00 · 2026-06-19 10:03:34 -07:00 · 2026-06-19 10:03:34 -07:00 · 2026-06-19 10:01:07 -07:00 · 2026-06-19 09:42:03 -07:00
28 changed files with 958 additions and 189 deletions
@@ -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`. **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 to `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 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)
@@ -23,7 +36,7 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r
 - 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).
 - To add a dev tool (pytest/ruff/pyright) into the env, use `uv sync --frozen --extra dev --extra gpu`, **never `uv pip install`** — `uv pip install` re-resolves and rewrites `uv.lock`, which silently bumped `transformers` to a build incompatible with the pinned `diffusers` (`cannot import name 'Qwen3VLForConditionalGeneration'`) and broke every `identify`/metadata import. Recovery: `git checkout uv.lock && uv sync --frozen --extra gpu --extra dev`. The `gpu` extra holds `diffusers`/`transformers`/`torch`, so a bare `uv sync` (no extras) removes them; `noai/__init__` is now **lazy** (PEP 562 `__getattr__`, so importing `identify`/`metadata` no longer pulls `watermark_remover`/torch), so a bare env breaks only when the removal pipeline is actually invoked, not on import. `maintain.sh`'s `uv sync --all-extras` also pulls the heavy `trustmark`/`lama` wheels (pytorch-lightning, onnxruntime) — fine on a good connection, but on flaky DNS sync only `--extra gpu --extra dev` and run the lint/test steps by hand.
- Metadata/C2PA tests assert against real committed fixtures in `data/samples/` (`chatgpt-*.png` = OpenAI C2PA, `firefly-1.png` = Adobe, `mj-*` = Midjourney IPTC, `doubao-1.png` = ByteDance Doubao with the China TC260 `<TC260:AIGC>` XMP label **and** a visible "豆包AI生成" text mark bottom-right; `grok-1.jpg` = xAI Grok with its EXIF-only `Signature:` blob + UUID `Artist` and no C2PA/SynthID/IPTC); synthetic byte blobs cover the JPEG/ISOBMFF format paths. The "non-AI / clean photo" control is no longer in `data/samples/` -- the `clean_photo` conftest fixture serves a verified-negative image from the corpus `neg/` set (skips if the corpus is absent).
+- Metadata/C2PA tests assert against real committed fixtures in `data/samples/` (`chatgpt-*.png` = OpenAI C2PA, `firefly-1.png` = Adobe, `mj-*` = Midjourney IPTC, `doubao-1.png` = ByteDance Doubao with the China TC260 `<TC260:AIGC>` XMP label **and** a visible "豆包AI生成" text mark bottom-right; `grok-1.jpg` = xAI Grok with its EXIF-only `Signature:` blob + UUID `Artist` and no C2PA/SynthID/IPTC; `flux-1.png` / `flux-1.jpg` = real Black Forest Labs FLUX.2 Playground output, signed C2PA (issuer "Black Forest Labs" + `trainedAlgorithmicMedia`) -- `flux-1.jpg` is the first committed **JPEG-with-C2PA** fixture, exercising the c2pa-python non-PNG reader path end to end; whether BFL hosted output also embeds the open DWT-DCT pixel watermark is UNRESOLVED -- our detector returns None on these fox samples, but they are high-texture carriers where even a known-embedded watermark fails the round-trip, see the content-fragility caveat in `docs/watermarking-landscape.md`); synthetic byte blobs cover the remaining JPEG/ISOBMFF format paths. The "non-AI / clean photo" control is no longer in `data/samples/` -- the `clean_photo` conftest fixture serves a verified-negative image from the corpus `neg/` set (skips if the corpus is absent).
 - SynthID reference corpus: `scripts/synthid_corpus.py` ingests labeled images into `data/synthid_corpus/`. The labeled `images/` (`pos/` `neg/` `cleaned/`) are **committed** (public repo -- review every image for private content before adding; `manifest.csv` is kept in sync with the files on disk, one row per tracked image); only the synthetic `refs/` calibration fills are gitignored. See its README for the collection protocol and verification oracles. **`cleaned/` examples must be produced by a CURRENT shipped removal method** -- the default SDXL img2img pass (optionally `--max-resolution`). Do NOT archive cleaned outputs from methods that are no longer in the pipeline (ctrlregen, the old text/face-protection, IP-Adapter FaceID, CodeFormer) or from the experimental opt-in paths (controlnet, face restore) as corpus examples; a cleaned reference should represent the canonical removal, and a removed method's output is not a reproducible example. Keep those experiment outputs in a local working dir, never in the committed corpus.

 ## Configuration
@@ -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).
@@ -46,7 +59,7 @@ Compact map. The full per-module detail (design decisions, tuned thresholds, cal
 - `_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).
 - `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. A detector-only removal test is insufficient — assert visual residual (the textured-shift tests).
 - `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).
- `invisible_watermark.py` — decodes the OPEN DWT-DCT watermarks (SD / SDXL / FLUX) via `imwatermark` (extra `detect`, pulls torch). Fragile: does not survive JPEG re-encode/resize, so it confirms origin only on pristine files.
+- `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 two diffusion pipelines selected by the explicit `pipeline` ctor arg, never inferred from `model_id`: `sdxl` (plain SDXL img2img) and `controlnet` (SDXL + canny ControlNet, **the DEFAULT since 2026-06-09**). Removal comes from the img2img `strength`; ControlNet only preserves text/face STRUCTURE — SynthID CAN survive controlnet on photoreal content at low strength. No face-restore extra ships, by validated decision (every restore approach looked MORE AI-generated).
 - `auto_config.py` + the content-detection layer were REMOVED 2026-06-09; `--auto` is a deprecated no-op (controlnet is the default pipeline and the adaptive polish is ON by default and self-gates to a no-op where there is no detail deficit).
@@ -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`.)
@@ -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`.

@@ -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 +103,10 @@ 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:** 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.
+
+**`_fixed/_aligned_alpha_map` and `extract_mask` return footprint-sized arrays, not full frames (memory):** the alpha-map helpers return the glyph-sized alpha **block** (`(gh, gw)` float32) plus its placement `(ax, ay, gw, gh)`, and `extract_mask` returns the box-sized glyph mask (`(loc.h, loc.w)` uint8) — both used to allocate a full `(h, w)` array that is read only inside the small glyph/box. A full-frame float32 alpha map is ~48 MB on a 12 MP frame and two were held at once during removal (fixed + aligned ≈ 96 MB of mostly-zeros); the box mask was a ~12 MB uint8 allocation rebuilt per text-mark `detect` on the memory-tight `identify` path. `_apply_reverse_alpha` consumes the block directly; the residual inpaint embeds it into one full-frame uint8 mask only at `cv2.inpaint` time (which needs a full-frame mask). Byte-identical to the old full-frame path — the block equals the old map's `[ay:ay+gh, ax:ax+gw]` slice and the box equals the old mask cropped to `loc.bbox` (regression-guarded by `tests/test_text_mark_memory.py`, which reconstructs the old full-frame path inline and asserts equality, so the proof survives a cv2/asset bump). `remove_watermark_reverse_alpha` tracks the winning `region` alongside `best_amap` to place that mask.
+
 ## `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).
@@ -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
@@ -5,8 +5,10 @@
 > no content was changed or summarized.

 Who embeds what, and whether it is locally detectable (so we know which gaps are fillable). See `identify.py` for what we read.
- **Locally detectable (open decoder, no key/API):** Stable Diffusion / SDXL / FLUX via `imwatermark` DWT-DCT (now covered by `invisible_watermark.py`). FLUX uses the same library (`black-forest-labs/flux2` `src/flux2/watermark.py`, 48-bit `0b001010101111111010000111100111001111010100101110`); SDXL is the diffusers `WATERMARK_MESSAGE` (`0b101100111110110010010000011110111011000110011110`). Caveat: fragile to re-encoding.
- **C2PA / IPTC (covered by the issuer/marker scan):** OpenAI, Google, Adobe Firefly, Microsoft (Designer + **Bing Image Creator** — collected 2026-05-24; Bing now runs Microsoft's own **MAI-Image** model, signs C2PA as "Microsoft", NOT OpenAI/DALL-E), and **Stability AI** (collected from Brand Studio / DreamStudio successor; signs C2PA as "Stability AI Ltd", no SynthID, no imwatermark on its current Stable Image model — issuer added to `C2PA_ISSUERS`). Still unsampled: Canva (its downloads are re-encoded design *exports* that strip C2PA, so a Canva "positive" is inconclusive — skipped), Getty, Shutterstock. Midjourney embeds NO C2PA and no invisible watermark (our `mj-*` sample carried only the IPTC tag).
+- **Locally detectable (open decoder, no key/API):** Stable Diffusion / SDXL / FLUX via `imwatermark` DWT-DCT (now covered by `invisible_watermark.py`). FLUX uses the same library (`black-forest-labs/flux2` `src/flux2/watermark.py`, 48-bit `0b001010101111111010000111100111001111010100101110`); SDXL is the diffusers `WATERMARK_MESSAGE` (`0b101100111110110010010000011110111011000110011110`). **Caveat: the `imwatermark` dwtDct decode is carrier-fragile on a broad class of real images, NOT just re-encode-fragile, and it is a POSITIVE-ONLY signal.** A clean encode->decode round-trip (no re-encode at all) recovers 48/48 bits on some carriers (random noise, chatgpt-1.png 48/48, firefly-1.png 45/48) but FAILS on many others — verified 2026-06-19 that a *known-embedded* watermark only round-trips 28-39/48 (below the safe `_MATCH_48` = 44 gate, random baseline ~24) on the FLUX fox sample (28), doubao-1.png (39), a 1024² minimalist-flat FLUX image (28), AND a **clean synthetic bright-flat fill with NO watermark at all (28)**. The failure does NOT track texture (firefly lapvar ~11 passes; the flat FLUX lapvar ~56 fails); it correlates with a degenerate decode where the raw bits read **all-ones (48/48 ones)** — which a clean synthetic image reproduces, so **all-ones is a CARRIER ARTIFACT, NOT a watermark signal** (a double-embed test also showed a pre-existing embed does not corrupt a second embed — no interference). Net: trust a `detect_invisible_watermark` hit, but treat a `None`/no-match as **inconclusive** whenever a positive-control embed on the same carrier does not first recover >=44/48. The 44 gate is a deliberate precision choice (lowering it would admit false positives).
+
+  Consequence for the FLUX hosted-output question (BFL Playground, FLUX.2 [pro] + FLUX.1 [dev], 2026-06-19): all samples carry the signed C2PA manifest (issuer "Black Forest Labs"); the open DWT-DCT decode returned `None`, but every available FLUX carrier (textured fox AND a minimalist-flat generation) failed the positive control (28/48), so the detector is blind on them and **whether BFL hosted output embeds the open pixel watermark is UNRESOLVED** (an earlier note here wrongly asserted it absent — overstated; a later note blamed "high texture" — also wrong, flat carriers fail too). What IS established: C2PA is the reliable FLUX identifier; the `_BITS_48` pattern is correct (round-trips on chatgpt/firefly/random). Resolving the hosted question needs a hosted FLUX carrier that first passes a >=44/48 positive control, which neither a textured nor a flat prompt produced — low priority (the open mark is only a stripped-metadata fallback).
+- **C2PA / IPTC (covered by the issuer/marker scan):** OpenAI, Google, Adobe Firefly, Microsoft (Designer + **Bing Image Creator** — collected 2026-05-24; Bing now runs Microsoft's own **MAI-Image** model, signs C2PA as "Microsoft", NOT OpenAI/DALL-E), **Stability AI** (collected from Brand Studio / DreamStudio successor; signs C2PA as "Stability AI Ltd", no SynthID, no imwatermark on its current Stable Image model — issuer added to `C2PA_ISSUERS`), and **Canva** (Magic Media signs C2PA as "Canva" + `trainedAlgorithmicMedia` with a generic `c2pa-rs` claim generator, no SynthID — issuer `b"Canva"` → "Canva (Magic Media)"; found on real production traffic 2026-06-19, which **disproved the earlier assumption** that Canva downloads are re-encoded exports that always strip C2PA). Still unsampled: Getty, Shutterstock. Midjourney embeds NO C2PA and no invisible watermark (our `mj-*` sample carried only the IPTC tag).

 **Samsung Galaxy AI** (Generative Edit / Sketch to Image / Portrait Studio on Galaxy S23 FE / S24 / S25, One UI 7+) signs C2PA as "Samsung Galaxy" with the standard `trainedAlgorithmicMedia` source type AND a proprietary `genAIType` marker; verified on real signed files 2026-05-29 (the standard scan catches the source type; `genAIType` additionally catches a Galaxy S24 file that omits it). It ALSO burns a **visible** localized wordmark into the pixels — a sparkle + "generated with AI" string in the bottom-LEFT corner (issue #37; the Italian "✦ Contenuti generati dall'AI" variant is calibrated) — removed by `samsung_engine.py` / `visible --mark samsung` (reverse-alpha, see the engine bullet); detection feeds `identify` as the medium `visible_samsung` signal. The string is locale-specific, so each locale needs its own captured alpha template.

@@ -14,7 +16,7 @@ Who embeds what, and whether it is locally detectable (so we know which gaps are

 **Black Forest Labs (FLUX)** API output signs C2PA: `claim_generator_info "Black Forest Labs API"` + a `c2pa.ai_generated_content` assertion + `trainedAlgorithmicMedia` (issuer `b"Black Forest Labs"` added to `C2PA_ISSUERS`, platform "Black Forest Labs (FLUX)").

-**ByteDance Volcano Engine (Volcengine)** — the cloud behind Doubao / Jimeng — signs its AI image output with a cert from `certificate_center@volcengine.com` + `trainedAlgorithmicMedia` (issuer `b"volcengine"` → "ByteDance (Volcano Engine)", platform "ByteDance (Doubao / Jimeng / Volcano Engine)"); note this is the C2PA-signed surface, distinct from the XMP/PNG TC260 `AIGC` label Doubao also uses. All three verified on real signed files 2026-05-29.
+**ByteDance Volcano Engine (Volcengine)** — the cloud behind Doubao / Jimeng — signs its AI image output with a cert from `certificate_center@volcengine.com` + `trainedAlgorithmicMedia` (issuer `b"volcengine"` → "ByteDance (Volcano Engine)", platform "ByteDance (Doubao / Jimeng / Volcano Engine)"); note this is the C2PA-signed surface, distinct from the XMP/PNG TC260 `AIGC` label Doubao also uses. All three verified on real signed files 2026-05-29. ByteDance's **international brand (BytePlus / Seedream / Seededit)** signs the SAME content as **"Byteplus Pte. Ltd."** — the bare `volcengine` needle missed it, so real BytePlus output was mis-attributed to "Adobe Firefly" (an incidental "Adobe XMP" toolkit string in the file's XMP, picked up by the fallback byte-scan once the clean manifest issuer matched nothing). Added issuer `b"Byteplus"` → org "BytePlus (ByteDance)" (platform resolves to the shared "ByteDance (Doubao / Jimeng / Volcano Engine)" label via the common `ByteDance` needle) so the clean manifest issuer attributes it directly; found on real production traffic 2026-06-19.
 - **EXIF/XMP generator tag (caught by `exif_generator`):** **Ideogram** writes EXIF `Make="Ideogram AI"` (collected 2026-05-24 — no C2PA, no SynthID, no imwatermark; the Make tag is the only signal).
 - **xAI / Grok — its own EXIF signature scheme, NOT C2PA (DETECTED by `metadata.xai_signature`, built 2026-05-26).**

@@ -1,6 +1,6 @@
 [project]
 name = "remove-ai-watermarks"
-version = "0.11.2"
+version = "0.11.4"
 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]
@@ -11,4 +11,4 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")


-__version__ = "0.11.2"
+__version__ = "0.11.4"
@@ -184,20 +184,27 @@ class TextMarkEngine:
    # ── Mask ────────────────────────────────────────────────────────────

    def extract_mask(self, image: NDArray[Any], loc: TextMarkLocation) -> NDArray[Any]:
-        """Build a full-image uint8 mask (255 = watermark glyph) for the box.
+        """Build a box-sized uint8 mask (255 = watermark glyph) for ``loc``.
+
+        Returns just the glyph mask of the located box (shape ``(loc.h, loc.w)``),
+        not a full-frame array: every caller immediately crops to ``loc.bbox``, so
+        allocating a full ``(h, w)`` mask and embedding the box was O(image) work
+        and memory for an O(box) result -- a wasted full-frame uint8 allocation on
+        each detect (~12 MB on a 12 MP frame, recomputed per text-mark detector on
+        the memory-tight identify path). The box mask is byte-identical to the old
+        full-frame mask cropped to ``loc.bbox``.

        Polarity-aware: the mark is a light, low-saturation gray rendered brighter
        than the local background (white top-hat), so a white-paper document is left
        untouched (nothing brighter than its surroundings is masked there).
        """
        c = self.config
-        h, w = image.shape[:2]
        x, y, bw, bh = loc.bbox
        # A degenerate ROI (a sliver from an extremely wide/short image) cannot hold
        # the mark and would feed cv2's GaussianBlur/morphology a ~1-px-tall array,
        # which can fault native code on some platforms. Skip the cv2 pipeline.
        if bh < 16 or bw < 16:
-            return np.zeros((h, w), np.uint8)
+            return np.zeros((bh, bw), np.uint8)
        # Normalize the ROI to 3-channel BGR (grayscale / BGRA would break axis=2).
        roi = image_io.to_bgr(image[y : y + bh, x : x + bw]).astype(np.float32)

@@ -216,11 +223,7 @@ class TextMarkEngine:
        glyph = cand.astype(np.uint8) * 255
        glyph = cv2.morphologyEx(glyph, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8))
        k = c.morph_open_size
-        glyph = cv2.morphologyEx(glyph, cv2.MORPH_OPEN, np.ones((k, k), np.uint8))
-
-        mask = np.zeros((h, w), np.uint8)
-        mask[y : y + bh, x : x + bw] = glyph
-        return mask
+        return cv2.morphologyEx(glyph, cv2.MORPH_OPEN, np.ones((k, k), np.uint8))

    # ── Detect ──────────────────────────────────────────────────────────

@@ -232,9 +235,8 @@ class TextMarkEngine:
        if image is None or image.size == 0:
            return det
        loc = self.locate(image)
-        mask = self.extract_mask(image, loc)
-        x, y, bw, bh = loc.bbox
-        box = mask[y : y + bh, x : x + bw]
+        box = self.extract_mask(image, loc)  # box-sized mask (== old full-frame cropped to bbox)
+        _x, _y, bw, bh = loc.bbox
        coverage = float((box > 0).sum()) / float(max(1, bw * bh))
        det.region = loc.bbox
        det.coverage = coverage
@@ -254,7 +256,15 @@ class TextMarkEngine:

    def _fixed_alpha_map(self, image: NDArray[Any]) -> tuple[NDArray[Any], tuple[int, int, int, int]] | None:
        """Place the template by fixed width-relative geometry (pixel-exact at the
-        captured width)."""
+        captured width).
+
+        Returns the glyph-sized alpha BLOCK (shape ``(gh, gw)``) plus its placement
+        ``(ax, ay, gw, gh)``, not a full-frame ``(h, w)`` map. The map is non-zero
+        only inside the glyph box and every consumer reads exactly that box, so a
+        full-frame float32 map was O(image*4 bytes) of mostly zeros -- ~48 MB on a
+        12 MP frame, and two were held at once (fixed + aligned). The block is
+        byte-identical to the old full-frame map's ``[ay:ay+gh, ax:ax+gw]`` slice.
+        """
        c = self.config
        at = self._alpha_template()
        if at is None:
@@ -268,22 +278,23 @@ class TextMarkEngine:
        else:  # bottom-left
            ax = min(max(0, int(c.alpha_margin_x_frac * w)), max(0, w - gw))
        ay = max(0, h - int(c.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), interpolation=cv2.INTER_LINEAR)
-        return amap, (ax, ay, gw, gh)
+        block = cv2.resize(at, (gw, gh), interpolation=cv2.INTER_LINEAR)
+        return block, (ax, ay, gw, gh)

    def _aligned_alpha_map(self, image: NDArray[Any]) -> tuple[NDArray[Any], tuple[int, int, int, int]] | None:
        """Register the captured template to the actual mark via a TM_CCOEFF_NORMED
-        scale + position search. Returns ``(alpha_map, glyph_bbox)`` or None."""
+        scale + position search. Returns the glyph-sized alpha BLOCK and its
+        placement ``(ax, ay, gw, gh)`` (see :meth:`_fixed_alpha_map` for why the
+        block, not a full-frame map), or None."""
        c = self.config
        at = self._alpha_template()
        sil = self._glyph_silhouette()
        if at is None or sil is None:
            return None
-        h, w = image.shape[:2]
+        w = image.shape[1]
        loc = self.locate(image)
        bx, by, bw, bh = loc.bbox
-        box_mask = self.extract_mask(image, loc)[by : by + bh, bx : bx + bw]
+        box_mask = self.extract_mask(image, loc)  # box-sized (== old full-frame cropped to bbox)
        expected = c.alpha_width_frac * w
        best: tuple[float, int, int, int, int] | None = None
        for scale in np.linspace(*c.alpha_align_search):
@@ -298,15 +309,31 @@ class TextMarkEngine:
            return None
        _, gw, gh, ox, oy = best
        ax, ay = bx + ox, by + oy
-        amap = np.zeros((h, w), np.float32)
-        amap[ay : ay + gh, ax : ax + gw] = cv2.resize(at, (gw, gh), interpolation=cv2.INTER_LINEAR)
-        return amap, (ax, ay, gw, gh)
+        block = cv2.resize(at, (gw, gh), interpolation=cv2.INTER_LINEAR)
+        return block, (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)``."""
+    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 the glyph-sized alpha BLOCK for ``region`` (x, y, w, h); outside
+        it the blend is a no-op (``(wm - 0)/(1 - 0) == wm``). 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, 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
@@ -334,16 +361,25 @@ class TextMarkEngine:
            return image.copy()
        best_out: NDArray[Any] | None = None
        best_amap: NDArray[Any] | None = None
+        best_region: tuple[int, int, int, int] | 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
-        if best_out is None or best_amap is None:  # pragma: no cover - maps is non-empty
+                best_residual, best_out, best_amap, best_region = residual, out, amap, region
+        if best_out is None or best_amap is None or best_region is None:  # pragma: no cover - maps is non-empty
            return image.copy()
        if residual_inpaint:
+            # Embed the glyph-sized alpha block into a full-frame uint8 mask only for
+            # the inpaint (cv2.inpaint needs a mask matching best_out). One uint8
+            # full-frame array, built once, vs the old two full-frame float32 maps;
+            # byte-identical to thresholding the old full-frame float32 map (zero
+            # outside the block, so the dilate/inpaint see the same mask).
+            ax, ay, gw, gh = best_region
+            rm = np.zeros(best_out.shape[:2], np.uint8)
+            rm[ay : ay + gh, ax : ax + gw] = (best_amap > c.residual_alpha_floor).astype(np.uint8) * 255
            kernel = np.ones((c.residual_dilate, c.residual_dilate), np.uint8)
-            rm = cv2.dilate((best_amap > c.residual_alpha_floor).astype(np.uint8) * 255, kernel)
+            rm = cv2.dilate(rm, kernel)
            best_out = cv2.inpaint(best_out, rm, c.residual_inpaint_radius, cv2.INPAINT_NS)
        return best_out
@@ -349,8 +349,12 @@ def _no_visible_mark_exit(source: Path) -> NoReturn:
        )
    else:
        console.print(
-            "  No AI provenance signal found either. If there is a logo or object to remove,\n"
-            "  target it directly with the region eraser:\n"
+            "  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)
@@ -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)
@@ -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:
@@ -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

@@ -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).
@@ -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:
    """
@@ -122,6 +122,20 @@ C2PA_AI_VENDORS: tuple[C2paAiVendor, ...] = (
    C2paAiVendor(
        b"volcengine", "ByteDance (Volcano Engine)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"
    ),
+    # ByteDance's international brand (BytePlus / Seedream / Seededit) signs its
+    # cert as "Byteplus Pte. Ltd." -- the bare ``volcengine`` needle misses it, so
+    # real BytePlus AI output was mis-attributed (an incidental "Adobe XMP" string
+    # in the file's XMP made it read "Adobe Firefly"). Adding the issuer means the
+    # clean manifest issuer matches "BytePlus (ByteDance)" directly. The platform
+    # string mirrors the volcengine row: both share the "ByteDance" needle, so the
+    # earlier row's label wins anyway -- they normalize together for clash
+    # detection. Verified on real signed files in production traffic, 2026-06-19.
+    C2paAiVendor(b"Byteplus", "BytePlus (ByteDance)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"),
+    # Canva Magic Media signs AI-generated images as "Canva" with a generic
+    # c2pa-rs claim generator + trainedAlgorithmicMedia; without this entry the
+    # source read AI but no platform was attributed. Verified on real signed files
+    # in production traffic, 2026-06-19. Canva does not use SynthID.
+    C2paAiVendor(b"Canva", "Canva", "Canva (Magic Media)", "Canva"),
    # Truepic is a C2PA signing authority, not an AI generator: no platform label,
    # never asserts is_ai (the verdict comes from the digital-source-type).
    C2paAiVendor(b"Truepic", "Truepic", None, None),
@@ -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
@@ -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

@@ -135,6 +135,11 @@ class TestVisibleCommand:
        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
@@ -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
@@ -66,6 +66,19 @@ class TestAttributePlatform:
        assert platform
        assert "Stability AI" in platform

+    def test_canva(self):
+        platform = _attribute_platform(["Canva"])
+        assert platform
+        assert "Canva" in platform
+
+    def test_byteplus_attributes_to_bytedance(self):
+        # ByteDance's intl brand signs as "Byteplus Pte. Ltd."; the registry maps
+        # it to the ByteDance platform (was mis-read as Adobe via an incidental
+        # "Adobe XMP" file string before the entry existed).
+        platform = _attribute_platform(["BytePlus (ByteDance)"])
+        assert platform
+        assert "ByteDance" in platform
+
    def test_empty_is_none(self):
        assert _attribute_platform([]) is None

@@ -216,6 +229,19 @@ class TestIdentifyRealSamples:
        assert r.is_ai_generated is True
        assert any("IPTC" in w for w in r.watermarks)

+    def test_flux_bfl_c2pa_png(self):
+        # flux-1.png: real Black Forest Labs FLUX.2 Playground output (signed C2PA).
+        r = identify(SAMPLES_DIR / "flux-1.png", check_visible=False)
+        assert r.is_ai_generated is True
+        assert r.platform == "Black Forest Labs (FLUX)"
+
+    def test_flux_bfl_c2pa_jpeg_via_reader(self):
+        # flux-1.jpg: same source as a JPEG -- the real committed JPEG-with-C2PA
+        # fixture that exercises the c2pa-python non-PNG reader path end to end.
+        r = identify(SAMPLES_DIR / "flux-1.jpg", check_visible=False)
+        assert r.is_ai_generated is True
+        assert r.platform == "Black Forest Labs (FLUX)"
+
    def test_clean_photo_is_unknown_not_clean(self, clean_photo: Path):
        r = identify(clean_photo, check_visible=False)
        assert r.is_ai_generated is None  # never asserted False
@@ -434,6 +460,18 @@ 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 ───────────────────────────────────────

@@ -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).

@@ -182,6 +182,37 @@ class TestC2PARealSamples:
        assert has_c2pa_metadata(out)
        assert "OpenAI" in extract_c2pa_info(out)["issuer"]

+    def test_extract_info_flux_jpeg_via_reader(self):
+        """Real committed JPEG-with-C2PA fixture: the non-PNG reader path works."""
+        info = extract_c2pa_info(SAMPLES_DIR / "flux-1.jpg")
+        assert info["has_c2pa"] is True
+        assert info["c2pa_manifest"].startswith("C2PA manifest store")  # reader, not chunk
+        assert "Black Forest Labs" in info["issuer"]
+        assert "trainedAlgorithmicMedia" in info["source_type"]
+
+    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):
@@ -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):
@@ -0,0 +1,134 @@
+"""Byte-identity guards for the text-mark engine memory optimization.
+
+The reverse-alpha text-mark engine used to allocate full-frame arrays where only
+the glyph footprint is ever read:
+
+  * ``extract_mask`` built a full ``(h, w)`` uint8 mask and every caller cropped
+    it to the located box;
+  * ``_fixed_alpha_map`` / ``_aligned_alpha_map`` each built a full ``(h, w)``
+    float32 alpha map that is non-zero only inside the glyph box, and two were
+    held at once during removal.
+
+Both now return footprint-sized arrays. These tests prove the new footprint-sized
+path is BYTE-IDENTICAL to the old full-frame path by reconstructing the old
+behavior inline from the new building blocks (so the proof survives a cv2/asset
+version bump, unlike a pinned output hash), and lock in the O(footprint) memory
+characteristic so a regression back to a full-frame allocation fails loudly.
+"""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+import pytest
+
+import remove_ai_watermarks.doubao_engine as D
+import remove_ai_watermarks.jimeng_engine as J
+import remove_ai_watermarks.samsung_engine as S
+from remove_ai_watermarks.doubao_engine import DoubaoEngine
+from remove_ai_watermarks.jimeng_engine import JimengEngine
+from remove_ai_watermarks.samsung_engine import SamsungEngine
+
+# (engine factory, engine module) for each reverse-alpha text mark.
+ENGINES = [
+    pytest.param(DoubaoEngine, D, id="doubao"),
+    pytest.param(JimengEngine, J, id="jimeng"),
+    pytest.param(SamsungEngine, S, id="samsung"),
+]
+
+
+def _watermarked(engine, module) -> np.ndarray:
+    """Composite the engine's real alpha glyph onto a flat mid-gray field at the
+    captured native width (so both placement candidates fire)."""
+    cfg = engine.config
+    nw = module._ALPHA_NATIVE_WIDTH
+    at = module._alpha_template()
+    gw, gh = int(cfg.alpha_width_frac * nw), int(cfg.alpha_height_frac * nw)
+    ax = (nw - int(cfg.alpha_margin_x_frac * nw) - gw) if cfg.corner == "br" else int(cfg.alpha_margin_x_frac * nw)
+    ay = nw - int(cfg.alpha_margin_bottom_frac * nw) - gh
+    amap = np.zeros((nw, nw), np.float32)
+    amap[ay : ay + gh, ax : ax + gw] = cv2.resize(at, (gw, gh))
+    a3 = amap[:, :, None]
+    img = np.full((nw, nw, 3), 100.0, np.float32)
+    return (a3 * np.array(cfg.alpha_logo_bgr, np.float32) + (1 - a3) * img).clip(0, 255).astype(np.uint8)
+
+
+@pytest.mark.parametrize(("factory", "module"), ENGINES)
+class TestExtractMaskFootprint:
+    def test_returns_box_sized_mask(self, factory, module):
+        eng = factory()
+        img = _watermarked(eng, module)
+        loc = eng.locate(img)
+        box = eng.extract_mask(img, loc)
+        assert box.dtype == np.uint8
+        # Shape == loc.bbox, i.e. the old full-frame mask's [y:y+bh, x:x+bw] crop.
+        assert box.shape == (loc.h, loc.w)
+        # Footprint, not full frame: the box is a tiny fraction of the image.
+        assert box.size * 4 < img.shape[0] * img.shape[1]
+
+
+@pytest.mark.parametrize(("factory", "module"), ENGINES)
+class TestAlphaMapFootprint:
+    def test_maps_are_footprint_sized_blocks(self, factory, module):
+        eng = factory()
+        img = _watermarked(eng, module)
+        for placed in (eng._fixed_alpha_map(img), eng._aligned_alpha_map(img)):
+            assert placed is not None
+            block, (ax, ay, gw, gh) = placed
+            assert block.dtype == np.float32
+            assert block.shape == (gh, gw)
+            # The placement stays fully inside the image (no clipping needed).
+            assert ax >= 0
+            assert ax + gw <= img.shape[1]
+            assert ay >= 0
+            assert ay + gh <= img.shape[0]
+            # O(footprint): far smaller than the frame.
+            assert block.size * 4 < img.shape[0] * img.shape[1]
+
+    def test_apply_reverse_alpha_equals_old_fullframe(self, factory, module):
+        """``_apply_reverse_alpha`` with the glyph block is byte-identical to the
+        old full-frame path: rebuild the full ``(h, w)`` map, run the old-style
+        full-frame reverse-alpha, and compare to the new block-based output."""
+        eng = factory()
+        img = _watermarked(eng, module)
+        h, w = img.shape[:2]
+        for placed in (eng._fixed_alpha_map(img), eng._aligned_alpha_map(img)):
+            assert placed is not None
+            block, region = placed
+            ax, ay, gw, gh = region
+
+            new_out = eng._apply_reverse_alpha(img, block, region)
+
+            # Old behavior: a full-frame map, indexed by region inside _apply_reverse_alpha.
+            full = np.zeros((h, w), np.float32)
+            full[ay : ay + gh, ax : ax + gw] = block
+            old_out = img.copy()
+            a3 = np.clip(full[ay : ay + gh, ax : ax + gw], 0.0, 1.0)[:, :, None]
+            logo = np.array(eng.config.alpha_logo_bgr, np.float32)
+            roi = old_out[ay : ay + gh, ax : ax + gw].astype(np.float32)
+            old_out[ay : ay + gh, ax : ax + gw] = np.clip(
+                (roi - a3 * logo) / np.clip(1.0 - a3, 0.25, 1.0), 0, 255
+            ).astype(np.uint8)
+
+            assert np.array_equal(new_out, old_out)
+
+    def test_residual_mask_equals_old_fullframe(self, factory, module):
+        """The residual inpaint mask built from the block embedded in a full-frame
+        canvas equals thresholding the old full-frame float32 map (zero outside the
+        block), so the dilate + inpaint see the same mask."""
+        eng = factory()
+        img = _watermarked(eng, module)
+        h, w = img.shape[:2]
+        cfg = eng.config
+        block, (ax, ay, gw, gh) = eng._fixed_alpha_map(img)
+
+        # New: embed the block into a uint8 canvas, then threshold.
+        new_mask = np.zeros((h, w), np.uint8)
+        new_mask[ay : ay + gh, ax : ax + gw] = (block > cfg.residual_alpha_floor).astype(np.uint8) * 255
+
+        # Old: a full-frame float32 map, thresholded everywhere.
+        old_full = np.zeros((h, w), np.float32)
+        old_full[ay : ay + gh, ax : ax + gw] = block
+        old_mask = (old_full > cfg.residual_alpha_floor).astype(np.uint8) * 255
+
+        assert np.array_equal(new_mask, old_mask)
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Author	SHA1	Message	Date
Victor Kuznetsov	f97fdc5b92	chore(release): v0.11.4	2026-06-19 10:06:04 -07:00
Victor KuznetsovandClaude Opus 4.8	4c8a57ec7b	docs: dwtDct detector is carrier-fragile (all-ones = artifact), FLUX open-mark unresolvable Final characterization after a positive-control sweep. The imwatermark dwtDct round-trip fails (28-39/48, below the 44 gate) not on "high texture" as a prior note claimed, but on a broad carrier class: the FLUX fox, doubao, a minimalist-FLAT FLUX generation, AND a clean synthetic bright-flat fill with NO watermark all fail identically. The degenerate all-ones decode is therefore a CARRIER ARTIFACT, not a watermark (the no-watermark synthetic image reproduces it; a double-embed test shows no interference). detect_invisible_watermark is positive-only: trust a hit, treat a None as inconclusive unless a same-carrier positive control first recovers >=44. Consequence: whether BFL hosted FLUX embeds the open DWT-DCT is unresolvable with this detector on the available carriers (textured AND flat FLUX both fail the control). C2PA stays the reliable FLUX signal. Low priority to chase further. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-19 10:03:34 -07:00
Victor KuznetsovandClaude Opus 4.8	a0a349cc66	docs: correct overstated FLUX open-watermark claim; record detector content-fragility Earlier notes asserted BFL hosted output has no open DWT-DCT watermark. That was overstated: the test carriers were high-texture fox images where a clean encode->decode round-trip of a KNOWN-embedded watermark recovers only 28-35/48 bits (below the safe 44 gate), so the detector would miss a present mark there -- the None is inconclusive, not proof of absence. Verified positive-control (2026-06-19): imwatermark dwtDct round-trips 48/48 on synthetic carriers and on chatgpt-1.png (48/48) / firefly-1.png (45/48), but FAILS on flux-1.png (28/48) and doubao-1.png (39/48). So invisible_watermark detection is a positive-only signal: trust a hit, treat a miss on busy content as inconclusive. Affects all open SD/SDXL/FLUX DWT-DCT detection. C2PA stays the reliable FLUX identifier; whether BFL hosted embeds the open mark is unresolved (needs a low-texture hosted sample). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-19 10:03:34 -07:00
Victor KuznetsovandClaude Opus 4.8	99e57c872f	perf(text-mark): footprint-sized arrays in reverse-alpha CPU path The reverse-alpha text-mark engine (Doubao/Jimeng/Samsung) allocated full-frame arrays where only the glyph footprint is ever read: - _fixed_alpha_map / _aligned_alpha_map each built a full (h, w) float32 alpha map non-zero only inside the glyph box, and two were held at once during removal (~96 MB of mostly-zeros on a 12 MP frame); - extract_mask built a full (h, w) uint8 mask that every caller cropped to the located box (~12 MB, rebuilt per text-mark detector on the memory-tight identify path). Both now return footprint-sized arrays: the alpha helpers return the glyph-sized block plus its placement (ax, ay, gw, gh), and extract_mask returns the box-sized mask. _apply_reverse_alpha consumes the block directly; the residual inpaint embeds it into one full-frame uint8 mask only at cv2.inpaint time (which needs a full-frame mask). remove_watermark_ reverse_alpha tracks the winning region alongside best_amap to place it. Peak allocation drops from O(image4)x2 + O(image) to O(footprint)x2 + one gated O(image1) uint8 mask -- a win every consumer gets, motivated by the 512 MB raiw.cc worker that OOMs on large decodes. GPU path untouched. Byte-identical to the old full-frame path (verified: 17 output hashes across the three engines, inpaint/no-inpaint, detect, and the real doubao-1.png fixture, unchanged before/after). tests/test_text_mark_memory.py guards it by reconstructing the old full-frame path inline and asserting equality, so the proof survives a cv2/asset bump, and pins the O(footprint) shape so a regression to full-frame fails loudly. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-19 10:01:07 -07:00
Victor KuznetsovandClaude Opus 4.8	9614615001	docs(landscape): confirm BFL hosted = C2PA-only on FLUX.1 [dev] too Lossless-PNG check across both BFL Playground model lines (FLUX.2 [pro] and FLUX.1 [dev]) confirms the open DWT-DCT pixel watermark is absent on hosted output regardless of model or container; only the signed C2PA manifest is present. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-19 09:42:03 -07:00
Victor KuznetsovandClaude Opus 4.8	9e307d020e	test(c2pa): add real FLUX.2 BFL C2PA fixtures (PNG + JPEG) flux-1.png / flux-1.jpg are real Black Forest Labs FLUX.2 [pro] Playground outputs (signed C2PA, issuer "Black Forest Labs" + trainedAlgorithmicMedia, manifests verified to contain no personal data). flux-1.jpg is the first committed JPEG-with-C2PA fixture, exercising the c2pa-python non-PNG reader path end to end. Regression tests assert both attribute to "Black Forest Labs (FLUX)". Also documents the verified finding (n=2, 2026-06-19): BFL's hosted output carries the signed C2PA manifest but NOT the open invisible-watermark DWT-DCT (decodes to degenerate all-ones, chance-level vs the FLUX reference) -- the open pixel mark is dev-inference-code-optional only. So a hosted FLUX.2 image is identified by C2PA alone, with no open-pixel fallback once C2PA is stripped. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-19 09:37:40 -07:00
Victor KuznetsovandClaude Opus 4.8	d4d9429328	feat(identify): attribute Canva and BytePlus C2PA; fix BytePlus->Adobe mislabel Mining the local production corpus (25,725 imgs) surfaced two AI vendors signing C2PA that the registry missed: - Canva (Magic Media) signed "Canva" + trainedAlgorithmicMedia -> detected AI but no platform attributed (disproves the old "Canva exports strip C2PA" assumption). - BytePlus (ByteDance international: Seedream/Seededit) signs "Byteplus Pte. Ltd."; the bare volcengine needle missed it, so its output was mis-attributed to "Adobe Firefly" via an incidental "Adobe XMP" string the fallback byte-scan picked up. Adding both to C2PA_AI_VENDORS lets the clean manifest issuer attribute them directly. Corpus re-run: 16 platform changes, all improvements (3 Adobe->ByteDance fixes, 4 None/TC260->ByteDance, 9 None->Canva), 0 regressions. An attempted signer-based attribution fallback was measured and dropped: it regressed 18 images (friendly ByteDance label -> raw Chinese cert org; IPTC tool name pre-empted). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-18 21:57:21 -07:00
Victor Kuznetsov	be566e8868	chore(release): v0.11.3	2026-06-18 17:28:21 -07:00
Victor KuznetsovandClaude Opus 4.8	9f6c26a439	refactor(c2pa): read manifests via official c2pa-python, keep byte-scan fallback 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>	2026-06-18 17:24:58 -07:00
Victor KuznetsovandClaude Opus 4.8	41f67973ce	fix(visible): inpaint mid-tone Gemini sparkle instead of a dark diamond The free `visible` path over-subtracted a faint Gemini sparkle on a mid-tone background into a darker-than-background brown diamond instead of removing it (2026-06-18 prod NPS report, "the watermark was not removed, just its color changed"). The existing over-subtraction guard only tripped when reverse-alpha drove a footprint pixel fully negative (the issue #30 dark-background black-pit case); on a mid-tone background the over-subtraction darkens the core well below the background without any pixel crossing zero, so the gate missed it and shipped the dark mark. Add a second over-subtraction signal to `_reverse_alpha_oversubtracts`: predict the reverse-alpha output at the bright core, (core - a*logo)/(1-a), and route to the footprint inpaint when it lands more than `_OVERSUB_DARK_MARGIN` (25) gray levels below the local background ring. Calibrated wide: clean removals predict within ~12 of background (demo_banana ~-1), the prod regression ~-40, the issue #30 dark case ~-82. Corpus-validated on the 479 detected Gemini images: 10 switch reverse-alpha to inpaint, all of them dark-diamond cases that improve or match; the other 469 stay byte-identical. demo_banana stays on the reverse-alpha path (byte-identical). Also crop both reverse-alpha helpers to the region they actually touch, a pure O(image) -> O(mark) win that is byte-identical to the full-frame math (a uint8<->float32 round-trip is exact): - `GeminiEngine._core_and_bg` converts only the footprint+ring crop to gray, not the whole frame (~70 ms -> 0.1 ms on a 12 MP image; it runs for both the alpha-gain estimate and the new gate). Verified identical across 479 images; detector confidence unchanged. - `TextMarkEngine._apply_reverse_alpha` computes the blend on the glyph crop only (`amap` is zero outside it, so the math is a no-op there): ~275 ms -> ~2 ms per placement on a 12 MP frame, up to 2 placements per removal. Verified identical across 142 Doubao/Jimeng placements. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-18 17:19:41 -07:00
Victor Kuznetsov	09fdb4544a	fix(invisible): preserve native output dimensions	2026-06-18 16:44:21 -07:00
Victor KuznetsovandClaude Sonnet 4.6	61aa76a591	perf(identify): decode the image once for all visible-mark detectors identify(check_visible=True) ran the Gemini-sparkle detector and the Doubao/Jimeng text-mark detector each with its own image_io.imread, so the same bitmap was fully decoded twice. On a memory-constrained host (the raiw.cc 512 MB web worker, which runs identify on every upload) that doubled the peak decode allocation and contributed to OOM restarts. Decode once in identify() and pass the BGR array to both detectors. The detect methods already accept an NDArray, so this only threads the pre-decoded array through: detect_sparkle_confidence and the two _visible_* helpers gain an optional image= param that, when None, preserves the old self-read behavior (so direct callers and the cv2-missing/unreadable paths are unchanged). Only the visible path is deduplicated; the optional check_invisible decoders are unaffected (and off on the web hot path). Adds a test asserting identify(check_visible=True, check_invisible=False) decodes exactly once. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-18 11:13:17 -07:00
dependabot[bot]	cd0a79df38	chore(deps): bump the minor-and-patch group with 5 updates (#50 ) Bumps the minor-and-patch group with 5 updates: \| Package \| From \| To \| \| --- \| --- \| --- \| \| [transformers](https://github.com/huggingface/transformers) \| `5.10.2` \| `5.12.1` \| \| [accelerate](https://github.com/huggingface/accelerate) \| `1.13.0` \| `1.14.0` \| \| [huggingface-hub](https://github.com/huggingface/huggingface_hub) \| `1.18.0` \| `1.19.0` \| \| [pytest](https://github.com/pytest-dev/pytest) \| `9.0.3` \| `9.1.0` \| \| [ruff](https://github.com/astral-sh/ruff) \| `0.15.16` \| `0.15.17` \| Updates `transformers` from 5.10.2 to 5.12.1 - [Release notes](https://github.com/huggingface/transformers/releases) - [Commits](https://github.com/huggingface/transformers/compare/v5.10.2...v5.12.1) Updates `accelerate` from 1.13.0 to 1.14.0 - [Release notes](https://github.com/huggingface/accelerate/releases) - [Commits](https://github.com/huggingface/accelerate/compare/v1.13.0...v1.14.0) Updates `huggingface-hub` from 1.18.0 to 1.19.0 - [Release notes](https://github.com/huggingface/huggingface_hub/releases) - [Commits](https://github.com/huggingface/huggingface_hub/compare/v1.18.0...v1.19.0) Updates `pytest` from 9.0.3 to 9.1.0 - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/9.0.3...9.1.0) Updates `ruff` from 0.15.16 to 0.15.17 - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.15.16...0.15.17) --- updated-dependencies: - dependency-name: transformers dependency-version: 5.12.1 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: minor-and-patch - dependency-name: accelerate dependency-version: 1.14.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: minor-and-patch - dependency-name: huggingface-hub dependency-version: 1.19.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: minor-and-patch - dependency-name: pytest dependency-version: 9.1.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: minor-and-patch - dependency-name: ruff dependency-version: 0.15.17 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: minor-and-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2026-06-18 10:07:41 -07:00
Victor KuznetsovandClaude Opus 4.8	4c6b56f888	lower(strength): drop vendor-adaptive floor to OpenAI 0.10 / Google 0.15 A 2026-06-14 oracle re-test on the deployed Modal controlnet worker (v0.10.0) cleared SynthID at OpenAI 0.10 (2 photoreal) and Google 0.15 (2 native 2816x1536, retiring the "native >= 0.30" guess), while a pixel sweep showed the 2026-06-04 cert floors (0.20/0.30) over-regenerated for no efficacy gain (Google MAE -20% at 0.15). Lowers OPENAI_STRENGTH 0.20->0.10, GEMINI_STRENGTH and UNKNOWN_STRENGTH 0.30->0.15. Caveats documented in watermark_profiles.py + docs: removal near this floor is seed-non-deterministic (a service must pin a verified seed), and the n=2 re-test did not cover flat-graphic hard cases. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-14 13:17:11 -07:00
Victor KuznetsovandClaude Opus 4.8	41a2af2ecb	fix(cli): preserve SynthID uncertainty in no-visible-mark message The 'no signal' branch of the visible no-mark path claimed 'No AI provenance signal found either', which reads as 'the image is clean'. A missing metadata proxy is not proof an invisible pixel watermark (SynthID) is absent: it cannot be detected once metadata is gone and may have been stripped upstream. The message now preserves that uncertainty and routes to both 'all' (regenerate pixels) and 'erase'. Regression-guarded by the SynthID/all asserts in test_cli.py. CLAUDE.md visible-command note updated to match. Also adds a 'Scope and non-goals' section (CLAUDE.md + README): removing AI-provenance marks on the user's own content is in scope; stripping stock/paid-content watermarks (Shutterstock/Getty/iStock, classifieds) is out of scope by principle, not by difficulty. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-13 19:30:49 -07:00
Victor KuznetsovandClaude Opus 4.8	d8cdc9f478	docs: correct stale strength-ladder values in remove_watermark docstring The convenience wrapper's docstring still quoted the pre-2026-06 ladder (0.10 OpenAI / 0.15 Google / 0.15 unknown). The live constants in watermark_profiles.py are 0.20 / 0.30 / 0.30, applied to both the controlnet and sdxl pipelines. Docstring only; behaviour was already correct via vendor_for_strength + resolve_strength. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-13 09:51:09 -07:00