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4 Commits
| Author | SHA1 | Message | Date | |
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 Victor Kuznetsov
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0e5a4cbc54 |
feat(visible): capture-less AI生成 pill (#54), inpaint fallback, MI-GAN backend (#56)
- Add the Jimeng-basic top-left "AI生成" pill as a CAPTURE-LESS mark (pill_engine.py): synthetic-silhouette edge-NCC detect + inpaint-only removal. Gated in remove_auto_marks: kept only when Jimeng is confirmed (TC260 metadata OR the bottom-right "★ 即梦AI" wordmark fired -- the wordmark keeps recall on metadata-STRIPPED uploads) AND Doubao did not fire. - Add an inpaint-fallback removal path + MI-GAN ONNX backend (migan extra, MIT, ~28 MB / ~1 GB peak -- droplet-friendly) alongside big-LaMa. New --method auto|reverse-alpha|inpaint (shared across visible/all/batch) and erase --backend migan; footprint_mask on each engine. - auto is deterministic: reverse-alpha for capture marks (recovers exact pixels, lighter -- measured cleaner than MI-GAN on structured backgrounds) and inpaint only for the capture-less pill. - --mark auto now removes EVERY detected mark in one pass (remove_auto_marks), so a Jimeng-basic image's top-left pill AND bottom-right wordmark both clear. - Bump 0.12.1 -> 0.13.0. Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |
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Victor Kuznetsov
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3aea21e632 |
feat(visible): Samsung Galaxy AI mark removal (bottom-left reverse-alpha, #37)
New samsung_engine.py mirrors the jimeng engine but anchors bottom-left; wired into watermark_registry, the CLI (--mark samsung / auto), and identify (visible_samsung, medium). visible_alpha_solve.py gains a corner=bl mode; samsung_alpha.png solved from @f-liva's flat captures. Calibrated for the Italian "Contenuti generati dall'AI" variant. Flat black/gray/white captures committed, real photos gitignored. Tests + docs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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Victor Kuznetsov
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e572767555 |
feat(visible): add Jimeng remover, fix Doubao outline defect, reproducible mask build
Visible-watermark work across all three corner-mark engines plus a committed,
reproducible alpha-build pipeline (scripts/visible_alpha_solve.py) fed by committed
solid black/gray/white captures.
- jimeng: new "即梦AI" wordmark remover (reverse-alpha + thin residual inpaint,
always NCC-aligned -- the mark re-rasterizes/jitters per image). Detect via glyph
silhouette NCC (0.45 threshold; does not cross-fire with Doubao). Registered in the
visible-mark catalog; `visible --mark jimeng` / `--mark auto`.
- doubao: fix a real production defect -- the shipped remover left a READABLE
"豆包AI生成" outline on real samples while detect() returned conf 0.0 (fooled by a
thin outline), so the test passed and the "56/56 clean" claim was detector-measured,
not visual. Root cause: under-estimated alpha + fixed-geometry-no-inpaint + tight
locate box. Rebuilt alpha (careful gray-self solve), always-align, thin inpaint,
widened locate box -> readable outline becomes faint texture-level traces.
- gemini: rebuild gemini_bg_{96,48} from our own controlled captures (validated NCC
0.9998 vs the prior third-party asset); removal re-verified clean, no behaviour change.
- tests: add textured-shift regression to both engines (guards the align-on-shift path
the Doubao defect exposed; lesson: a detector-only removal test is insufficient,
assert visual residual).
- docs: CLAUDE.md, README, capture READMEs and docstrings synced; stale
"exact/pixel-exact/56-clean" claims removed.
Also includes a SynthID label-wording clarification in identify.py/cli.py
("SynthID pixel watermark" -> "SynthID watermark, inferred from C2PA metadata").
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Victor Kuznetsov
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58bdf51c59 |
Visible-watermark registry: reverse-alpha-only Doubao + Gemini, exact native recovery (#28)
* fix(trustmark): gate detection on re-encode durability to kill false positives TrustMark's wm_present flag is a BCH validity check that spuriously validates on a content-correlated fraction of un-watermarked images (AI textures trip it more than camera photos). On a 1343-image set all 20 raw detections were false, several on Gemini/OpenAI/Doubao output that cannot carry Adobe's watermark, with random-bytes secrets. A genuine TrustMark is a durable soft binding that survives re-encoding, so detect_trustmark now re-decodes after a mild JPEG round-trip and requires the same schema both times. Every observed false positive collapsed under this gate; the second decode runs only on the rare hit. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(identify): Samsung Galaxy AI, FLUX, ByteDance C2PA; fix C2PA substring FP Detection extensions verified on real signed files (2026-05-29): - Samsung Galaxy AI: signer attribution via a new _SIGNER_C2PA_PLATFORM (Samsung Galaxy / ASUS Gallery) kept separate from the capture-camera _DEVICE_C2PA_PLATFORM so a Galaxy AI edit (device cert + AI source type) does not trip the camera-vs-AI integrity clash. Plus metadata.samsung_genai: the proprietary genAIType marker in PhotoEditor_Re_Edit_Data, a medium- confidence AI-editing signal (samsung_only branch). - Black Forest Labs (FLUX) and ByteDance Volcano Engine (Doubao/Jimeng) added as C2PA issuers + issuer->platform mappings. - fix: C2PA presence required only the bare 4-byte 'c2pa' substring, which false-positives on compressed pixel data (a recompressed PNG IDAT re-flagged C2PA after its manifest was correctly stripped). New c2pa_marker_in() requires the JUMBF wrapper (jumb+c2pa) or the C2PA uuid box; applied in identify + metadata. Verified: all 535 real C2PA files carry jumb. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(doubao): gate detection on text structure to cut ~95% of false positives (#23) Coverage alone over-fired: any textured bottom-right corner cleared the threshold, so the detector false-positived on ~28% of arbitrary images. The real '豆包AI生成' mark is six glyphs in one row, so detect now also requires the text-structure signature (_glyph_structure): many connected components, no single dominant blob, concentration in a thin horizontal band. False positives dropped 343 -> 17 across the corpus while keeping real-mark recall and the doubao-1.png sample. Also accept a no-op force kwarg for remover-interface symmetry. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(samsung): add Samsung Galaxy AI visible-badge remover New samsung_engine.py removes the bottom-left sparkle + localized 'AI-generated content' badge that Galaxy AI tools stamp. Mirrors the Doubao locate->mask->inpaint pattern but bottom-left, with a dual-polarity top-hat mask (the badge is light-on-dark or dark-on-light). Detection gates on a band + left-anchor signature (the Doubao CJK-component gate does not transfer: Latin badge letters connect into few blobs). Explicit-only -- tuned on few real badges with a ~4% FP floor, so it is not used in auto. Synthetic byte-blob fixtures (real badges are user content, not shipped). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(visible): unified known-watermark registry + LaMa inpaint backend watermark_registry.py is a single catalog of known visible marks, each tying {usual location, in_auto flag, recovery strategy, detect adapter, remove adapter}: gemini (reverse-alpha, exact), doubao, samsung. cmd_visible is now registry-driven (best_auto_mark for --mark auto; mark_keys() feeds the CLI choices) -- the per-mark _run_doubao/_run_samsung helper branches are gone. Cross-engine confidences are not comparable, so the gemini adapter applies the corpus-validated 0.5 sparkle threshold for auto arbitration (its engine flag is loose and weakly fired ~0.36 on Doubao text, hijacking auto). --backend auto|cv2|lama chooses background reconstruction for the mask-based marks; auto = LaMa when onnxruntime is present, else cv2. For LaMa the mask is the FILLED glyph bounding box (sparse glyph masks leave anti-aliased edges behind). cv2 stays the zero-dependency fallback. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: watermark registry, Samsung/FLUX/ByteDance detection, LaMa backend, trustmark gate Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(doubao): exact reverse-alpha removal from captured alpha map The Doubao '豆包AI生成' mark is a fixed semi-transparent white overlay, so given its alpha map the original pixels are recovered exactly: original = (wm - a*logo)/(1-a) -- no inpaint hallucination. The alpha map + logo colour were solved from real black+gray Doubao captures on a controlled background: on black captured = a*logo, and the black/gray pair solves a per-pixel without assuming the logo colour (a_max~0.65, logo near-white); the white capture cross-validates (mark vanishes to a flat fill). Bundled as assets/doubao_alpha.png + geometry constants. remove_watermark_reverse_alpha applies it scaled to image width; exact at the captured width, so the registry routes doubao through it only when reverse_alpha_available (width within the calibrated band) and the mark is detected, falling back to mask inpaint (cv2/LaMa) otherwise. A light residual inpaint cleans the sub-pixel rescaling error. Add captures at more resolutions to widen exact coverage. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * refactor(visible): reverse-alpha only -- drop inpaint removal + heuristic detection Per the principle that we only remove/detect what we can do exactly, the visible-mark path is now reverse-alpha only: - Doubao detect is reverse-alpha-consistent: match the bundled alpha glyph silhouette against the corner via TM_CCOEFF_NORMED (DETECT_NCC_THRESHOLD 0.4) -- keys on the '豆包AI生成' SHAPE, not coverage/structure heuristics. FP 7/1243 (0.6%). Removes the cv2 inpaint path + the _glyph_structure gate. - Registry is reverse-alpha only: dropped the cv2/LaMa backend (_glyph_remove, _lama_box_inpaint, default_backend, --backend) and the Samsung entry. Doubao outside the alpha resolution band is skipped, never inpainted. - Removed samsung_engine.py + tests + --mark samsung (no alpha map captured; Samsung C2PA/genAIType metadata detection in identify is unaffected). - The universal erase --region (cv2/LaMa) is unchanged -- arbitrary-region inpainting stays a user-directed tool, separate from the known-mark registry. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(doubao): NCC sub-pixel alignment -> reverse-alpha at any resolution A pure width-scale of the captured alpha map is only sub-pixel-accurate at the captured width and leaves a faint ghost elsewhere. remove_watermark_reverse_alpha now registers the alpha glyph to the actual mark via a TM_CCOEFF_NORMED scale+position search (_aligned_alpha_map) before inverting the blend, so the single 2048 capture works at any resolution -- verified clean on the 1773x2364 (3:4) corpus size, the biggest coverage gap (23 files). reverse_alpha_available is now just 'asset present' (no width band); the registry still gates removal on detect so a clean corner is never touched. Drops the _ALPHA_WIDTH_TOLERANCE gate. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(doubao): keep native recovery exact -- fixed geometry at captured width Integer-pixel NCC alignment landed ~1px off at the captured width, degrading the otherwise-exact native reverse-alpha (synthetic recovery error 0.94 -> 1.39). remove_watermark_reverse_alpha now uses exact width-relative geometry within _ALPHA_NATIVE_BAND of the captured width and the NCC search only off it -- best of both: native back to 0.94, other resolutions still aligned. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(doubao): harden alignment -- try fixed+aligned, keep least residual (56/56) On a faint/busy-background mark the NCC alignment peak can wander a few px off the true mark and leave a residual (2/56 real corpus files). Off the captured width, remove_watermark_reverse_alpha now builds BOTH the fixed-geometry and the NCC-aligned alpha map, applies each, and keeps whichever leaves the least residual mark (re-detect confidence on the bare reverse-alpha) -- geometry wins on faint marks, alignment on clear ones, no magic threshold. Real-file round-trip now removes 56/56 detected Doubao clean across every corpus resolution (was 54). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * perf(doubao): skip residual inpaint at native width for exact recovery At the captured width the fixed-geometry reverse-alpha is pixel-exact, so inpainting over it only replaced exactly-recovered interior pixels with a cv2 hallucination -- measured worse on a textured background (native error vs true bg 1.6 reverse-alpha-only vs 2.6 with the old always-on full-footprint inpaint). Native now returns the bare recovery untouched; off-native, where NCC alignment is only sub-pixel-approximate, the footprint inpaint stays to clean the seam. Real round-trip still 56/56 across all corpus resolutions; negatives 0/60, Gemini unaffected. Add test_native_returns_exact_reverse_alpha_no_inpaint as the regression guard. Sync CLAUDE.md + README (the table cell and prose described the pre-NCC "skipped off native / cv2-LaMa" behavior, now stale). Gitignore the session scheduled_tasks.lock, and add the text-protection research note. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |