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Author SHA1 Message Date
Victor Kuznetsov 719c7e9313 chore(gitignore): ignore local-only data/spaces working dir 
21k user-pull images under data/spaces/ were untracked but not ignored, so a
stray `git add -A` could have committed them. Add the ignore entry alongside the
other local-data paths; the dir stays local analysis only, never a committed corpus.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
 2026-06-22 09:32:22 -07:00
Victor Kuznetsov d5dd24140c fix(qwen): native-geometry img2img + pipeline-aware strength; record dropped auto/mixed/Z-Image leads 
- watermark_remover: _build_qwen_kwargs now passes explicit height/width (via
  _qwen_target_size, floored to /16). Without it QwenImageImg2ImgPipeline defaults to
  1024x1024 and silently squishes non-square inputs, distorting the scene and garbling text.
- watermark_profiles: resolve_strength gains a `pipeline` arg + a Qwen strength ladder
  (_QWEN_VENDOR_STRENGTH, Gemini 0.25), so `--pipeline qwen` gets its certified floor
  automatically; retires the manual "pass --strength 0.25 for Gemini on qwen" workaround.
- fidelity_metrics: replace per-face nearest matching (collided on multi-face images when a
  variant dropped a face, corrupting the identity metric) with a collision-free one-to-one
  assignment (assign_faces_one_to_one). lapvar/LPIPS were always bbox-anchored and immune.
  Regression-guarded by tests/test_fidelity_matching.py.
- docs: record the measured outcomes of the qwen-improvement arc. The Qwen ControlNet
  face-fix is CLOSED (no permissive Qwen detail/tile ControlNet exists; canny carries edges,
  not skin grain). The `--pipeline auto` router + faces+text mixed dual-pass were prototyped
  and DROPPED (controlnet wins faces AND display text: abba CER 0.114 vs qwen 0.379).
  Z-Image-Turbo was tried and dropped (same regeneration limits). qwen stays a manual opt-in;
  controlnet is the default for everything.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
 2026-06-20 21:52:56 -07:00
Victor Kuznetsov 295e7ada2b chore: project review (dev tools in extras, dep upgrades, optional-deps guard, stale cleanup) 
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
 2026-06-09 17:03:17 -07:00
Victor Kuznetsov 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>
 2026-06-05 10:27:44 -07:00
Victor Kuznetsov 411ef16ec3 feat: GFPGAN face-identity restoration post-pass 
Add an optional, commercial-safe face-restoration post-pass that recovers
face identity the diffusion removal pass drifts (canny holds structure, not
likeness) while still scrubbing the pixel watermark in the face regions.

- face_restore.py: GFPGANer singleton (CPU unless CUDA), the basicsr
  torchvision.transforms.functional_tensor shim, and the pure feather
  _composite_faces helper (unit-tested without the model). GFPGAN
  re-synthesizes each face from a StyleGAN2 prior, so composited face pixels
  are GAN-generated (no watermark, no pixel-copy) -- oracle-clean at weight 0.5
  with identity preserved.
- InvisibleEngine.remove_watermark: restore_faces / restore_faces_weight,
  best-effort, auto-skips when the extra is absent or no face is detected.
- CLI --restore-faces/--no-restore-faces + --restore-faces-weight on
  invisible/all/batch (on by default).
- restore extra (gfpgan/facexlib/basicsr), numpy<2-pinned (scipy<1.18,
  numba<0.60) and kept out of `all`; basicsr needs Python <3.13 + setuptools<69
  to build, so pin .python-version 3.12.

Commercial-safe: GFPGAN Apache-2.0, RetinaFace MIT. The CodeFormer alternative
is non-commercial and is not shipped. The earlier IP-Adapter FaceID layer was
removed (footgun: needs high strength, corrupts faces at the low removal
strength).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
 2026-06-03 16:59:28 -07:00
Victor Kuznetsov 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>
 2026-05-31 12:20:19 -07:00
Victor Kuznetsov 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>
 2026-05-29 19:49:09 -07:00
test-user bc3228d387 feat(visible): Doubao text-mark removal + universal region eraser 
Add deterministic, CPU-only removal of the visible Doubao "豆包AI生成" mark and
a position-agnostic region eraser for any other visible watermark/logo.

- doubao_engine.py: locate (geometry, scales with width) + polarity-aware
  white-top-hat glyph mask + cv2 inpaint; coverage-gated detection and a
  dense-text safety guard. No GPU, ~30ms.
- region_eraser.py + `erase` command: inpaint arbitrary --region box(es).
  Default cv2 backend (no deps); optional big-LaMa via onnxruntime (`lama`
  extra, Carve/LaMa-ONNX, model downloaded on first use, never bundled).
- cli `visible --mark auto|gemini|doubao`: auto routes by detector confidence.
- tests for both engines; seed previously-unseeded CLI image fixtures to stop
  the Doubao detector flaking on random corners.
- .gitignore: doubao_capture/{seeds,captures} scratch (alpha-map calibration).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-26 21:31:51 -07:00
test-user 03fb460f77 Track the labeled SynthID corpus; complete metadata-source test coverage 
Corpus images were gitignored (local-only). The negatives were reviewed and
cleared for publishing, so the labeled set is now committed (regular git, 65 MB
across 25 files) -- making the removal regression set reproducible and CI-able.

Corpus:
- Track data/synthid_corpus/images/ (pos 9, neg 15, cleaned 1); keep only the
  synthetic refs/ calibration fills gitignored.
- Reconcile manifest.csv to the on-disk files: 117 -> 25 rows (92 dangling rows
  for removed images pruned; dedup left one cleaned output, f6dd47a5).
- Rewrite the corpus README layout/policy (images committed; review every image
  for private content before adding -- public repo, permanent history).

Test fixtures:
- Remove data/samples/not-ai-1/2/3 (personal iPhone photos, incl. GPS EXIF).
- Add the clean_photo conftest fixture serving a verified-negative image from
  the corpus neg/ set; repoint the three "non-AI / clean photo" tests onto it
  (skips if the corpus is absent).

Metadata-source coverage (close the last sub-variant gaps):
- c2pa digitalSourceType: algorithmicMedia (procedural, not flagged AI) and
  compositeWithTrainedAlgorithmicMedia (AI + SynthID proxy).
- exif_generator: EXIF Artist and ImageDescription fields (Software/Make/XMP
  CreatorTool were already covered).

All 8 metadata-source kinds are now tested at both the unit and identify()
level. 313 tests pass. CLAUDE.md updated (corpus tracked, clean_photo fixture).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-25 14:46:47 -07:00
test-user f07ce10c72 feat(metadata): SynthID-source detection, C2PA parser consolidation, corpus + tests 
Detect SynthID-bearing images via their C2PA companion: a manifest signed by a
SynthID-using vendor (Google/OpenAI) on AI-generated content implies an
invisible SynthID pixel watermark. Verified end-to-end against the vendor
oracles (openai.com/verify, Gemini "Verify with SynthID").

- metadata: synthid_source() + synthid_watermark verdict in get_ai_metadata,
  surfaced as a `metadata --check` callout. Format-agnostic (PNG caBX parser +
  JPEG/WebP/AVIF/HEIF/JXL binary scan).
- constants: SYNTHID_C2PA_ISSUERS {Google, OpenAI}; +opened/placed actions.
- c2pa: single CBOR-aware parser (_cbor_text_after) replaces glitchy regex
  (fixes fGPT-4o claim_generator); removed duplicate _scan_png_c2pa_chunk from
  metadata; shared synthid_verdict / synthid_vendors_in helpers.
- corpus: scripts/synthid_corpus.py ingest tool + data/synthid_corpus/
  (manifest tracked, images gitignored) for a labeled reference set.
- tests: +38 across C2PA parser internals, extract/inject round-trip, ISOBMFF
  container stripping, all IPTC AI markers, and invisible watermark strength
  tiers (SynthID/StableSignature/TreeRing/StegaStamp/RingID/RivaGAN/...).

Pixel-level SynthID detection remains out of reach locally (Google's decoder is
proprietary); a from-scratch spectral pilot confirmed it does not separate real
content. See CLAUDE.md for the full evaluation.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-24 11:32:46 -07:00
test-user 7eb32fedee refactor: enforce strict linting and type checking across codebase 
- Expand ruff rules (B, S, SIM, RET, COM, C4, G, PT, PIE, T20, DTZ, ICN, TCH, RUF, ANN)
- Switch pyright to strict mode with relaxed test environment
- Replace try-except-pass with contextlib.suppress throughout
- Move type-only imports into TYPE_CHECKING blocks
- Replace ambiguous Unicode chars (en dash, multiplication sign, Greek alpha) with ASCII
- Move color-matcher from base deps to [gpu], remove unused requests dep
- Add pyright to dev deps, update dependabot to uv ecosystem
- Fix hardcoded version in test_version, unused unpacked vars in tests
- Update maintain.sh, CLAUDE.md, .gitignore, .claude/settings.json
- Remove obsolete .agents/rules/project.md
- Upgrade all dependencies (Pygments vulnerability fix)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-01 11:42:42 -07:00
test-user e5d8970add Add project files, tests, and documentation for GitHub release 
- CLI with visible, invisible, all, metadata, and batch commands
- Gemini watermark removal via reverse alpha blending
- Invisible watermark removal via diffusion regeneration (SynthID, TreeRing)
- AI metadata stripping (EXIF, PNG text, C2PA)
- Face protection (YOLO/Haar) and analog humanizer
- 137 tests covering all CLI modes and core engines
- Ruff and Pyright clean
 2026-03-25 11:15:05 -07:00