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Author SHA1 Message Date
Victor KuznetsovandClaude Opus 4.8 b1189549b8 feat(invisible): controlnet default, unified strength, retire --auto, add --model/--guidance-scale 
Overhaul the diffusion-removal surface around a single robust default and a
complete, consistent CLI.

Pipeline + strength:
- controlnet is now the DEFAULT pipeline (CLI --pipeline + both engine ctors).
  With the certified higher strength it clears both photoreal and flat-graphic
  content, whereas plain SDXL left SynthID on flat graphics.
- Rename the plain-SDXL profile default -> sdxl; "default" stays as a back-compat
  alias (normalize_profile + a click callback that warns).
- Unify the strength ladder: resolve_strength applies ONE vendor-adaptive ladder
  (the certified controlnet floors OpenAI 0.20 / Google 0.30 / unknown 0.30) to
  both pipelines. sdxl is the weaker remover on its own hard case (flat fills),
  so the certified floor is the right floor for it too.

CLI completeness:
- Add --model (HF model id) to invisible + batch (was only on all) and
  --guidance-scale (CFG) to all three diffusion commands; both were library
  knobs the CLI did not expose.
- Flip --adaptive-polish to ON by default (it self-gates to a no-op where there
  is no detail deficit, so default-on is safe).
- Share --pipeline / --strength / --model / --guidance-scale as single
  decorators so invisible/all/batch keep an identical surface; the --strength
  help is derived from the strength constants (strength_default_help) so it can
  never drift from the ladder.

Removals:
- Delete the auto_config content-detection planner + its YuNet/DBNet assets
  (~2.6 MB): with controlnet always the pipeline and the polish self-gating, the
  face/text/edge detection no longer changed behavior. --auto is now a deprecated
  no-op that only warns (the polish it enabled is the default).

Docs (README, CLAUDE.md, docs/synthid.md) updated throughout; added an
InvisibleEngine Python API example. Tests cover the alias warnings, the
polish default, and the --model/--guidance-scale wiring.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
 2026-06-09 12:40:45 -07:00
Victor KuznetsovandClaude Opus 4.8 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 KuznetsovandClaude Opus 4.8 6d11c11b52 feat(auto): DBNet text detector, Real-ESRGAN upscaler, batch --auto 
Three content-quality features for the invisible/all/batch pipeline.

DBNet text detector (auto_config): replace the MSER text heuristic with
PP-OCRv3 differentiable-binarization via cv2.dnn.TextDetectionModel_DB,
using a bundled 2.4 MB Apache-2.0 model (en/cn detection nets are
byte-identical, so it ships language-neutral). cv2.dnn is core OpenCV, so
no new pip dep. MSER stays as the fallback when the model can't load.
Validated on real images: matches MSER everywhere and additionally catches
the Doubao CJK mark MSER missed; routing decisions unchanged otherwise.

Real-ESRGAN upscaler (new upscaler.py, esrgan extra): optional
pre-diffusion super-resolution for the min-resolution floor upscale, loaded
via spandrel (MIT, no basicsr) with BSD-3-Clause weights downloaded on
first use. New --upscaler {lanczos,esrgan} on invisible/all/batch; default
stays lanczos and the engine falls back to lanczos when the extra is absent
or the model errors (never breaks removal). It is a manual opt-in knob (the
auto plan never selects it) -- as a generic GAN it sharpens photo/texture
content strongly but can degrade faces (the diffusion pass regenerates
them) and thin text, documented accordingly.

batch --auto: wire the content-adaptive --auto (+ --adaptive-polish) into
cmd_batch. The plan is recomputed per image and the invisible engine is
cached per resolved pipeline (default/controlnet), so a mixed directory
builds at most one engine of each kind. Verified end-to-end: 3 mixed
images routed correctly with only 2 pipeline loads (controlnet reused).

ruff + strict pyright(src/) clean; 558 tests pass.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
 2026-06-04 16:04:33 -07:00
Victor KuznetsovandClaude Opus 4.8 9bd2c17cc4 feat(auto): content-adaptive --auto quality mode, Phase 1 
Add `auto_config.plan(image_path) -> AutoConfig`, the first step of the
invisible/all pipeline: it inspects the input image (before the diffusion model
loads) and picks the quality modes so the run adapts to content. Quality-priority
routing -- ControlNet (text/face-structure preservation) is the default, skipped for
plain SDXL only on a clearly structure-less image; GFPGAN face restore when a face is
present; a mild sharpen + grain polish when a smoothing pass ran. Exposed as `--auto`
on `all`/`invisible` (`_apply_auto`; explicit flags override via click's parameter
source). Not wired into batch (its engine is cached per-mode).

Detection is cv2-only and torch-free (~100 MB peak RSS, a few ms): OpenCV YuNet
(`cv2.FaceDetectorYN`, MIT, 232 KB model bundled in assets/) for faces, a Canny
edge-density + MSER heuristic for text/structure (a rough Phase-1 placeholder; DBNet
via cv2.dnn is the planned upgrade). ZERO new pip deps. Designed to run wherever the
pipeline runs -- the raiw.cc Modal GPU worker -- never on the 512 MB web host.

Real-ESRGAN-via-Spandrel upscaling (a new `esrgan` extra) and an adaptive
Laplacian-variance polish are deferred to later phases.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
 2026-06-03 20:52:17 -07:00
Victor KuznetsovandClaude Opus 4.8 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 KuznetsovGitHubClaude Opus 4.8
 		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 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