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>
Apply fixes from a full-repo review (code, tests, docs).
Security / correctness:
- Clamp attacker-controlled PNG/caBX chunk lengths to the remaining file
size in metadata.py and noai/c2pa.py (a malformed length no longer drives
a multi-GB read); skipped chunks seek instead of read.
- noai/isobmff.strip_c2pa_boxes is now fail-safe on a malformed box: return
the original bytes with a warning instead of silently truncating the tail,
so metadata --remove can no longer emit a corrupt file.
- doubao_engine._fixed_alpha_map clamps the glyph box to the image (no crash
on degenerate width-vs-height).
- watermark_remover._run_region_hires gates the phaseCorrelate offset on
response and magnitude (a spurious shift no longer garbles text) and drops
the generator after a CPU fallback (no MPS/CPU device mismatch).
Robustness:
- gemini_engine, doubao_engine, region_eraser normalize grayscale and RGBA
inputs to BGR at the engine entry points.
- image_io.imwrite returns False on an unwritable path (matches cv2).
- invisible_engine guards a None imread result before use.
- trustmark_detector._decoder uses a double-checked threading lock.
- ctrlregen.tiling.tile_positions raises on overlap >= tile.
- humanizer chromatic shift no longer wraps opposite-edge pixels.
- identify OpenAI caveat keyed on the normalized vendor, not a substring.
- Remove the dead "visible --detect-threshold" CLI option.
- publish.yml verifies the release tag matches the package version.
Docs:
- README strength 0.05 to 0.10; .env.example HF_TOKEN marked optional;
doubao_capture README updated to reverse-alpha-only; CLAUDE.md synced with
the new behaviors and the batch command.
Tests: new test_security_clamp.py for the read clamp and isobmff fail-safe;
erase CLI coverage; integrity-clash rule 2 end-to-end; multi-tag EXIF
survival and cross-format strip guards; channel/size, tiling, humanizer, and
imwrite regressions. Full suite 493 passed, 2 skipped; ruff and pyright src/
clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the default text-protection path. Differential Diffusion froze text in
latent space, which left SynthID intact inside text (violating remove-everywhere)
and still softened sub-8px strokes (VAE latent limit). _run_region_hires instead
scrubs the whole image, then re-scrubs each detected text block at high resolution
and feather-composites it back: every pixel is regenerated (watermark removed
everywhere) while small text stays crisp (high-res strokes span >1 latent cell).
merge_text_regions + feather_paste are pure and unit-tested; each re-scrubbed
patch is phase-correlated back to the original crop to null the ~1-2px round-trip
offset. Synthetic 18px multilingual text: text-region SSIM 0.28 -> 0.48, visually
garbled -> readable across Latin/Cyrillic/CJK. Legacy _run_differential /
build_change_map remain but are no longer the default. Prod use still requires
confirming via the SynthID oracle that re-scrubbed text zones read watermark-free.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A corpus audit surfaced China TC260 AIGC-labeled images that `identify`
missed. Three detection gaps in `aigc_label`, all fixed:
- raw-JSON `{"AIGC":{...}}` in JPEG EXIF (UserComment): brace-matched from
the scan head with `json.raw_decode`, gated on a TC260 field like the
PNG-chunk path. (Doubao-class output via that export surface.)
- XMP attribute form `TC260:AIGC="{...}"` (PicWish): folded into the
element regex as a second alternation.
- TC260 XMP packet appended after a large `IDAT`, past the 1 MB scan
window: `scan_head` now appends late PNG metadata chunks via
`_png_late_metadata`, mirroring the existing ISOBMFF late-box scan.
Adds `scripts/corpus_gap_scan.py`: runs `identify` over a corpus, writes
the per-file report CSV, and flags `unknown` files that carry a known
marker in their metadata region (the audit that found these gaps).
Scanning only the metadata region — not the whole file — avoids the
random short-token collisions inside compressed PNG/JPEG streams.
On the local corpus this lifts 3 files from `unknown` to AI (China AIGC)
and leaves zero false gap candidates. Synthetic piexif/PngInfo fixtures
cover all three forms.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* 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>
The text-protection detector scaled every image to a fixed 736 px long side, so
small text on large canvases (e.g. ~16 px on 2048) was downscaled below the
detector and missed -> deformed by the SDXL pass (issue #14). Detect at the
native long side capped at 1536, never upscaled (_detection_input_size, a pure
unit-tested helper). Detection is script-agnostic (DB segments regions, not
characters), so this is language-agnostic: a new benchmark
(scripts/text_detection_benchmark.py) measures recall across Latin/Cyrillic/CJK/
Hangul/Arabic/digits x sizes x canvas -> overall hit-rate 0.91 -> 1.00, worst
cell (2048/16 px) 0.06 -> 1.00. Docs updated.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Add cross-platform CI test matrix, PyPI classifiers
CI: new test.yml runs lint (ubuntu) + a test matrix (ubuntu/macos/windows
x py3.10/3.12, core+dev, GPU tests skip) on push to main and PRs, closing the
gap where only the release publish.yml ran (ubuntu, no tests). Add PyPI
classifiers (OS/Python/topic). README Tests badge, CLAUDE.md CI note.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Make availability tests reflect installed deps, not assume gpu extra
The new core+dev CI matrix has no diffusers, so the invisible-engine
availability tests (asserting is_available() is True unconditionally) and the
two mocked invisible CLI tests (whose command gates on is_available before the
mock) failed. Assert availability == actual importability of torch+diffusers,
and patch the CLI availability gate so the mocked-engine tests run regardless of
the gpu extra.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Make `pyright src/` strict-clean via a hybrid: pure-logic files are fully typed
(piexif gets a local typings/ stub; PIL info-dict loops guard isinstance(key, str);
progress returns Callable[..., None]; availability checks use importlib.util.find_spec
instead of unused imports), while the irreducibly-untyped cv2/torch/diffusers boundary
files carry a documented per-file `# pyright:` relax pragma (or a ctrlregen
executionEnvironment) that disables only the unknown-type rules. Public ndarray-returning
signatures on the relaxed engines are annotated NDArray[Any] so strict consumers (cli.py)
stay clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
aigc_label now reads the TC260 label from a raw-JSON `AIGC` PNG tEXt chunk
(as Doubao/ByteDance write it, with no namespaced XMP marker) in addition to
the `<TC260:AIGC>` XMP block, via a shared _parse helper gated on a TC260 field
so a generic AIGC key cannot false-positive. New huggingface_job() reads the
hf-job-id PNG chunk; identify surfaces it as a medium-confidence hf_job signal
(parallel to the visible sparkle, never overriding a hard metadata verdict).
Both wired into has_ai_metadata/get_ai_metadata; the PNG save whitelist already
strips them on removal. Found by auditing 646 corpus originals: 28 AIGC and 3
hf-job files the library previously reported as Unknown.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Mirror protect_faces: protect_text defaults to True in invisible_engine and
watermark_remover, so the SDXL pipeline detects text per image and switches to
Differential Diffusion only when glyphs are found. Text-free inputs fall back to
plain img2img with no differential-pipeline load, so the autonomy is free. The
CLI now exposes a single off-switch --no-protect-text instead of the positive
flag, keeping the interface minimal.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
SDXL img2img regenerates every pixel, so small text and CJK glyphs deform
at the strengths that defeat SynthID (issue #21). With --protect-text a
CJK-native PP-OCRv3 detector (2.4 MB ONNX, cv2.dnn, no torch, cached on
first use) locates text regions and the pass switches to the SDXL
Differential-Diffusion community pipeline: a per-pixel change map keeps
text regions largely intact while the background is regenerated to strip
the watermark. Gated to the SDXL default model; falls back to plain
img2img with a warning when unavailable.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The Roadmap is the project TODO; shipped features (Integrity Clash, streaming-MP4
scan window, meta-box XMP blanking) no longer belong under "not yet implemented".
Removed them and kept the still-open remainder as its own item (AVIF/HEIF Exif
*item* inside the meta box). Net open TODO: SynthID v2 regression test, local
SynthID pixel detector, grow the SynthID corpus, real non-PNG C2PA fixtures,
pyright maintenance debt, meta-box Exif item, Canon/Samsung device signers,
Resemble PerTh (dead end), video pipeline.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
HEIF/AVIF store XMP as a meta-box `mime` item whose bytes live in mdat/idat, out
of reach of the top-level uuid/jumb box stripper. An AI-label XMP packet there
(TC260 AIGC, IPTC "Made with AI", IPTC 2025.1) was therefore left in place.
isobmff.blank_ai_xmp_packets locates each XMP packet by its <?xpacket begin ...
end?> delimiters and, if it carries an AI marker (_AI_LABEL_MARKERS), overwrites
it with spaces of the SAME length. Equal length means no box size or iloc offset
shifts -- the coded image stays bit-for-bit intact, the item stays structurally
valid, only the AI label content is destroyed. Plain (non-AI) XMP is left alone,
mirroring the top-level XMP-uuid content match. Wired into remove_ai_metadata's
ISOBMFF branch after strip_c2pa_boxes.
Chosen over exiftool (a non-bundled binary dep) to stay pure-Python and
droplet-compatible; over full iinf/iloc surgery to avoid offset-rewrite
corruption risk. The AI labels we target are all XMP, so this closes the
practical gap. An Exif *item* inside the meta box (rare) still needs iinf/iloc
surgery or exiftool -- documented.
4 new tests (TestMetaBoxXmpBlanking): AI packet blanked (same length, marker
gone, surrounding image bytes intact), plain XMP preserved, no-packet no-op, and
end-to-end remove_ai_metadata on a .heic.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Provenance detection no longer relies on a fixed first-MB read. In a streaming /
non-faststart MP4 the C2PA manifest sits AFTER a multi-megabyte mdat, beyond the
1 MB scan window, so it was missed.
- isobmff.scan_c2pa_region(path): a file-seeking top-level box walker that
returns the payloads of uuid/jumb (provenance) boxes, seeking past mdat by
size without reading it -- works on multi-GB files. Returns b"" for
non-ISOBMFF or on read error. Mirrors the box-size encoding of the existing
in-memory _iter_top_level_boxes (largesize / size==0).
- metadata.scan_head(path, size): the shared input for every C2PA/AIGC/IPTC
byte scan -- first __TEXT __DATA __OBJC others dec hex bytes plus, for ISOBMFF, the late provenance-box
payloads. Behavior-neutral (f.read(size)) for non-ISOBMFF inputs.
- Routed all six metadata scan sites (has_ai_metadata, aigc_label,
iptc_ai_system, synthid_source, exif_generator XMP, get_ai_metadata
soft-binding) and identify's head read through scan_head.
6 new tests: late box found by scan_c2pa_region / scan_head, the fixed window
provably misses it, non-ISOBMFF -> b"", front-placed (faststart) regression.
The remaining gap stays documented: EXIF/XMP stored as items inside the meta
box (AVIF/HEIF stills) still needs meta-box surgery or exiftool.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Surface contradictions between independent provenance signals instead of
collapsing to a single verdict -- a strong tell of spoofed, transplanted, or
laundered metadata. Inspired by arXiv:2603.02378.
Two rules in the new _integrity_clashes helper:
- Conflicting AI-origin attributions: two or more distinct AI vendors named by
independent generator stamps (e.g. a C2PA OpenAI manifest on an image whose
EXIF says Make="Ideogram AI").
- Camera + AI: a camera-capture C2PA device (Pixel/Leica/Sony/Nikon/Truepic)
coexisting with an AI-generation marker -- a genuine capture is not AI.
High-precision by design: only hard generator stamps feed it (C2PA issuer when
the source is AI, SynthID proxy, EXIF/XMP generator, IPTC AISystemUsed, xAI,
AIGC). The fuzzy visible sparkle and the open invisible watermark are excluded
-- the latter can be a by-product of our own SDXL removal pass. Vendor
normalization (_vendor_of over _AI_VENDOR_TOKENS) keeps consistent signals from
clashing (C2PA "Google (Gemini)" + SynthID-Google agree); the C2PA vendor is
read from the issuer attribution, not the resolved platform, so a camera label
like "Google Pixel" cannot mis-normalize to an AI vendor.
Surfaced as ProvenanceReport.integrity_clashes (red in the table view, included
in --json). 19 new tests; all real single-origin fixtures (chatgpt/firefly/
doubao/grok/mj) verified to produce zero clashes (false-positive guard).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
README:
- surface a lawful-use / no-liability disclaimer near the top
- reword two feature bullets away from detection-evasion framing
("bypass AI image classifiers" -> neutral post-processing; drop the
platform-targeting language from the "Made with AI" bullet)
- Legal table, each corrected against the primary text:
- CA AB 2655 was struck down on Section 230 ONLY (Kohls v. Bonta,
E.D. Cal., Aug 2025); the court did not reach the First Amendment
(the companion AB 2839 was separately enjoined on 1A grounds)
- COPIED Act: add the bill number (S. 1396, 119th Cong.)
- South Korea AI Framework Act: in force 22 January 2026 (exact date)
CLAUDE.md: sync the South Korea date to 22 January 2026.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Metadata-strip feature now lists the audio/video container coverage shipped in
v0.6.0-v0.6.4 (MP4/MOV/M4V/M4A via ISOBMFF box walker; WebM/MP3/WAV/FLAC/OGG
losslessly via ffmpeg).
- Roadmap updated: the AVIF/HEIF item now reflects that top-level XMP/C2PA boxes
and non-ISOBMFF audio/video are handled, with only meta-box-item EXIF/XMP left
(needs exiftool). Added the open backlog: multi-signal "Integrity Clash"
reporting (arXiv:2603.02378), Canon/Samsung device signers pending a real
sample, the streaming-MP4 scan-window limit, and Resemble PerTh audio as
evaluated-but-infeasible.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Broadens metadata provenance coverage at the detection and container-strip level.
Detection:
- C2PA soft-binding `alg` -> forensic-watermark vendor (Adobe TrustMark,
Digimarc, Imatag, Steg.AI, Microsoft, ...) via C2PA_SOFT_BINDINGS +
soft_binding_vendors_in(); names the watermark vendor even when the watermark
itself can't be decoded.
- IPTC Photo Metadata 2025.1 AI-disclosure XMP fields (AISystemUsed etc.) via
iptc_ai_system() + IPTC_AI_FIELD_MARKERS.
- Adobe TrustMark open keyless decoder (trustmark_detector.py, optional extra
`trustmark`) -- the watermark behind Adobe Durable Content Credentials.
Detects provenance, not AI origin, so it does not assert is_ai.
Removal / containers:
- isobmff.strip_c2pa_boxes now also drops a top-level XMP uuid box that carries
an AI label (matched by AI-marker content, byte-order-robust; plain XMP kept).
- remove_ai_metadata routes MP4/MOV/M4V/M4A (and any ftyp-sniffed ISOBMFF)
through the box stripper; raises a clear error for non-ISOBMFF audio/video
(WebM/MP3/WAV) instead of crashing in the image path.
Tests: soft-binding scan, IPTC element/attribute/presence, MP4 + M4A detect/
strip, ISOBMFF XMP surgical strip, content-sniff, unsupported-container guard,
TrustMark absent-safety + identify integration. ruff clean; pyright clean on
all new modules.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
xAI Grok (Aurora) images carry no C2PA/SynthID/IPTC -- their only provenance
signal is an EXIF pair: ImageDescription "Signature: <base64>" + a UUID Artist.
Verified stable across 3 genuine generations (a real download previously read
as unknown / "no AI metadata").
- metadata.xai_signature(): matches the Signature blob + UUID Artist pair;
wired into has_ai_metadata, get_ai_metadata, and identify (platform
"xAI (Grok / Aurora)").
- data/samples/grok-1.jpg: real Grok fixture (neutral content; the Artist UUID
is the public image id, not PII).
- Tests: synthetic-fixture unit tests, real-sample assertion, identify
integration (322 passing).
Docs (research refresh, May 2026):
- C2PA 2.4 Durable Content Credentials (soft-binding re-discovery after the
embedded manifest is stripped).
- New AI-labeling laws, primary-source verified: EU AI Act Art 50 (2026-08-02),
South Korea AI Framework Act Art 31(3), California AB 853.
- Hedge removal claims: defeating the SynthID verifier is not forensic
invisibility (arXiv:2605.09203); cite SynthID-Image (arXiv:2510.09263).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Move the raiw.cc call-to-action above the sponsor ask and drop the
misleading "free web service" framing: visible-watermark and metadata
removal are free, invisible removal runs on paid cloud GPUs. Also point
no-GPU users to the hosted service from the invisible-removal feature
bullet, where the GPU requirement is stated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Threat model: replace the unverified deployment list (Gemini 3 Pro /
Nano Banana Pro / Imagen 4 / Veo) with the source-verified scope -- SynthID
across Imagen / Veo / Lyria plus Gemini app outputs (>10B items by Dec 2025),
and attribute the 136-bit payload to the paper's SynthID-O variant.
openai-images-2 sample: note the file predates the 19 May 2026 SynthID
rollout across ChatGPT / Codex / API, and that openai.com/verify is now the
public oracle (still no local decoder).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- fal's llms.txt confirms fast-sdxl is stabilityai/stable-diffusion-xl-base-1.0,
the exact checkpoint the local CLI defaults to -> local == prod weights.
Recorded in CLAUDE.md and README.
- README How it works + sample README: replace the old downscale->upscale
description with native-resolution processing (matches the #10 fix);
document --max-resolution as an opt-in OOM cap.
- README roadmap: idna already bumped (uv-secure clean).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the documented gap where EXIF/XMP fields inside AVIF/HEIF/JXL went
unparsed. metadata.exif_generator extracts the EXIF Software/Artist tag
(via PIL+piexif, which opens AVIF natively) and the XMP CreatorTool (via a
container-agnostic raw-byte scan that also covers HEIF/JXL that PIL can't
open), and matches against AI_GENERATOR_TOKENS so only generator names
(Firefly, DALL-E, Midjourney, ComfyUI, ...) fire -- a plain 'Adobe
Photoshop' or 'GIMP' tag is not flagged.
identify() surfaces it as a high-confidence signal and uses it for
platform attribution when no C2PA names a platform, so an AVIF/HEIF whose
only AI signal is an EXIF/XMP generator tag is now caught.
Validated with synthesized fixtures (the 'no positive fixtures' blocker
was self-imposed): real AVIF and JPEG written with EXIF Software via PIL,
plus an XMP CreatorTool raw-scan fixture. Zero false positives across the
109-image corpus (real iPhone photos carry no AI generator token).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Research found one locally-fillable detection gap: Stable Diffusion, SDXL,
and FLUX all embed an open DWT-DCT watermark via the invisible-watermark
(imwatermark) library -- a PUBLIC decoder, no secret key, unlike SynthID.
New invisible_watermark.py decodes the known fixed patterns (verified
against upstream source: diffusers SDXL WATERMARK_MESSAGE, FLUX.2
src/flux2/watermark.py, and the 'StableDiffusionV1' default string) and
identify() reports the scheme as a high-confidence signal.
Verified locally end-to-end: embedding SDXL's exact 48-bit message and
decoding it back recovers 48/48 bits; a clean image and our own fal-SDXL
outputs decode to ~21/48 (no match). Caveat baked into the report: the
watermark is fragile -- gone after JPEG q90 -- so it confirms origin only
on pristine files; absence is never proof.
imwatermark is an optional dep (extra 'detect'; pulls non-headless opencv),
so the import is guarded and the signal is skipped when absent. CLI
--no-visible now means metadata-only (skips both pixel-domain detectors).
Also records the broader watermarking landscape in CLAUDE.md: which
services are locally detectable (SD/SDXL/FLUX), C2PA-covered (Bing/Canva/
Getty/Shutterstock unsampled), or proprietary-only like SynthID (Amazon
Titan/Nova, Kakao). Midjourney embeds neither C2PA nor an invisible mark.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
New 'identify' command and identify.py module: upload an image, get one
ProvenanceReport answering where it was made and what watermarks it
carries. Aggregates every locally-readable signal:
- C2PA Content Credentials -> generating platform (issuer + generator).
- IPTC digitalSourceType 'Made with AI' (Meta and others).
- Embedded SD/ComfyUI generation parameters (local pipelines).
- SynthID metadata proxy (Google / OpenAI C2PA companion).
- Visible Gemini sparkle (cv2 fallback for the stripped-metadata case),
promoted only at confidence >= 0.5 (corpus-tuned: Gemini sparkles
score >= 0.56, non-sparkle <= 0.49).
is_ai_generated is True or None, never asserted False -- stripped
metadata leaves no local proof of a clean origin, so absence of signals
is reported as 'unknown' with an explicit caveat. The SynthID *pixel*
watermark remains locally undecodable; the report says so.
Non-PNG containers (JPEG/WebP/AVIF/HEIF/JXL) get the same issuer +
generator attribution via a binary scan (the caBX parser is PNG-only).
The cv2 dependency is isolated in gemini_engine.detect_sparkle_confidence
so identify.py stays type-clean. CLI supports --json and --no-visible.
Validated against the 109-image corpus: 14/14 positives flagged AI,
93/94 negatives clean (the one 'neg' flagged is a Meta image that
genuinely carries the IPTC tag -- correct), zero true errors.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Captures the forward plan so a future session picks it up: local pixel
detector is blocked pending a generation API or raw watermarked dataset
(spectral methods shown insufficient); grow the oracle-labeled corpus;
replace synthetic non-PNG C2PA fixtures with real ones; and the maintenance
debt (idna bump, strict-pyright cleanup) needed for a green maintain.sh.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
SD-1.5 dreamshaper at 768 px did not defeat SynthID v2 on Gemini 3 Pro
outputs (verified May 2026 via Gemini app's "Verify with SynthID"). Switch
the default invisible engine to SDXL at 1024 px, matching the raiw-app
production config (strength 0.05, steps 50). Drop the SD-1.5 pipeline.
Metadata layer: add C2PA UUID and IPTC AI marker byte-scan detection
across all formats, plus an ISOBMFF box walker (noai/isobmff.py) that
strips top-level C2PA uuid and JUMBF jumb boxes from AVIF/HEIF/JPEG-XL
containers without re-encoding.
README gets a Legal table and a Threat-model section about SynthID v2's
136-bit payload. CLAUDE.md tracks the SD-1.5 regression as historical
context.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- metadata --check now shows claim_generator, c2pa_spec, digital_source_type,
c2pa_actions, signer instead of empty table for C2PA-only files
- reuses existing extract_c2pa_chunk() from noai/c2pa.py — no more duplicate
PNG chunk parsing or full-file reads
- adds data/samples/openai-images-2/amur-leopard.png: real gpt-image-2 output
with C2PA manifest signed by OpenAI OpCo LLC / Trufo CA (spec 2.2.0)
- removes stale data/samples/nano-banana-1/2.png (no longer referenced)
- updates README: new Images 2.0 row in supported models table
- documents known text-degradation limitation in CLAUDE.md
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove opencv-python from [gpu] extra (conflicts with headless in base deps)
- Add graceful fallback in 'invisible' and 'all' commands when GPU deps missing
- Cache InvisibleEngine in batch mode (avoid reloading model per image)
- Fix --humanize help text (was '0.0-1.0', actual range is 0-6.0+)
- Fix stale docstring referencing non-existent [invisible] extra
- Add [gpu] extra install instructions to README
- Fix broken NeuralBleach placeholder URL in Credits
- Rewrite README for SEO: Nano Banana, SynthID, Made with AI, C2PA keywords
- Add Supported Models table with 7 AI services
- Add 'Made with AI' label removal to features
- Rename sections for search discoverability
- Add samples: ChatGPT/DALL-E, Midjourney, Adobe Firefly
- Reorganize data/samples with flat structure and clear naming
- Unify 'all' defaults to match 'invisible' (strength=0.02, steps=100)
- Reorder CLI docs: 'all' command first, individual commands second
- HuggingFace token is now documented as optional
- Remove 'additional setup' label from invisible section
Changes since 0.1.0:
- Fix phantom model param bug in invisible/all commands
- Fix macOS SSL certificate issue for YOLO downloads
- Use temp file in 'all' pipeline to hide intermediate output
- Add legal disclaimer and fix license attribution
- Add troubleshooting and upgrade docs to README
- Expand test suite to 137 tests covering all CLI modes
- Clean up dependencies and pyright config
- Add disclaimer section to README (research/education purposes)
- Remove incorrect Apache-2.0 license claim from ctrlregen docstrings
- Expand Credits with CtrlRegen and NeuralBleach attribution
- Add license info (MIT) for GeminiWatermarkTool and NeuralBleach
- 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
Victor Kuznetsov