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>
Coverage audit (pytest --cov) found real, non-model logic at 0%/low cover.
Add unit tests that need no model download:
- img2img_runner.py 0% -> 100%: the MPS->CPU fallback orchestration, mocked
via injected load_pipeline/reload_on_cpu callables. Guards the production
behavior hit this session (native-res SDXL OOMs on MPS, must retry on CPU;
non-MPS errors must propagate; "mps"-worded error on a cpu device must not
reload).
- ctrlregen/tiling.py 0% -> 40%: the pure tile math (tile_positions,
make_blend_weight, resize_center_crop) that decides how large images are
split and blended. (run_tiled stays model-bound, untested.)
- isobmff.py 93% -> 100%: size==0 (box-to-EOF) and truncated 64-bit largesize
parsing branches for AVIF/HEIF/JXL C2PA stripping.
- c2pa.py: non-PNG-signed .png reads as clean (has_c2pa_metadata /
extract_c2pa_chunk) instead of mis-parsing.
309 tests pass (+23). Document in CLAUDE.md that these pure helpers are
unit-tested without downloads so future sessions don't skip them as "ML".
No src/ change, no release.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The native-vs-downscale decision in InvisibleEngine.remove_watermark (the
issue #10/#15 fix: max_resolution=0 must not pre-downscale, since any
downscale both loses quality and lets SynthID survive) had no test. Extract
it into a pure helper invisible_engine._target_size(w, h, max_resolution)
and cover it with tests/test_invisible_engine.py::TestTargetSize so a
re-introduced forced downscale fails CI instead of silently regressing #15.
Also:
- Clamp the short side to >=1 in _target_size: extreme aspect ratios (e.g.
5000x3 with --max-resolution 1024) truncated it to 0 and crashed
image.resize(). Pre-existing in the inline math; fixed now that it is a
named, tested function.
- Consolidate the two duplicated temp-file save blocks into one
unconditional save (behavior unchanged: the EXIF-transposed image is
still always persisted before WatermarkRemover reloads it by path), and
drop the now-redundant `_tmp_path is not None` guard in finally.
- Bump version 0.5.3 -> 0.5.4 (pyproject, __init__, uv.lock); document the
helper as the regression guard in CLAUDE.md.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Concrete data point from the 2026-05-25 gpt-image SDXL run: native
1254x1254 fp32 OOMs at the UNet step (not just VAE) on a 20 GB MPS
ceiling, and img2img_runner auto-falls back to CPU and completes
(slow, weight-identical, still defeats SynthID). enable_vae_tiling()
alone does not prevent it. Fast Mac workarounds: fp16 on MPS or
--max-resolution; neither is the default.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add manifest row for the 4ef377bd -> f6dd47a5 chain: a gpt-image-2 sample
(openai.com/verify: SynthID + C2PA detected) cleaned via v0.5.3 `all` at
native 1254x1254 (prod-equivalent SDXL base, strength 0.05, 50 steps).
openai.com/verify reports SynthID NOT detected after the run, re-confirming
that the #10 native-resolution default defeats OpenAI SynthID and resolving
the #15 root cause (older SD-1.5/768px downscale default did not).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
data/samples/doubao-1.png is the real #13 sample: carries the China TC260
<TC260:AIGC> XMP label and a visible '豆包AI生成' text mark (bottom-right).
Grounds the AIGC detection on a real file (alongside the synthetic tests)
and serves as the fixture for visible-watermark removal work.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2048x2048 PNG carrying China's TC260 <TC260:AIGC> label; identify reports
it as a China AIGC-labeled generator (TC260). Reference fixture for manual
re-verification of the TC260 detection path -- the automated tests use
synthetic blobs, so nothing depends on this file being present.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- feat(identify): detect the China TC260 <TC260:AIGC> XMP label (Doubao
and other China-served generators); reports platform + ContentProducer.
Removal already strips it via the existing metadata cleaner.
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>
- fix(invisible): process at native resolution by default; the forced
downscale-to-1024 -> upscale-back round-trip was the main quality loss
(#10). Matches the raiw.cc backend (fal fast-sdxl = sdxl-base-1.0).
New --max-resolution opt-in cap for GPU/MPS memory.
- docs: verified fal checkpoint, native-res, gpt-image-2 SynthID.
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>
The invisible pipeline force-downscaled inputs >1024px to 1024 before
diffusion, then upscaled the result back -- a lossy round-trip that was
the main cause of the quality loss reported in #10. The hosted raiw.cc
backend (fal fast-sdxl) does no pre-downscale, and at strength ~0.05
SDXL img2img doesn't need it.
Default is now native resolution (max_resolution=0). New --max-resolution
flag (invisible / all / batch) re-introduces an opt-in long-side cap only
to bound GPU/MPS memory on very large inputs.
Addresses #10. End-to-end quality/removal not re-verified locally (no GPU
here); matches raiw-app's proven production config.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
#11 left the import block un-sorted (ruff I001); reorder so diffusers
precedes the local ctrlregen import.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
`cv2.imread(..., IMREAD_COLOR)` was silently stripping the alpha channel
on RGBA inputs, and `cv2.imwrite` then wrote opaque 3-channel PNGs — so
images with transparent backgrounds came back with an opaque-black (or
white) background and the sparkle area baked in as a solid blob.
Read the source with `IMREAD_UNCHANGED`, keep the alpha plane out of the
detection/inpaint path (those still operate on BGR), and rejoin alpha at
save time. The detected watermark bbox is also zeroed in the alpha plane
so the sparkle region becomes transparent rather than an opaque artifact.
Applies to `visible`, `all`, and `batch` modes. RGB-only inputs and JPEG
outputs are unaffected.
Based on #9 by @eskibars. Replaces the os.execl(..., "-c", repr-string)
restart (used after the CUDA-torch auto-install) with os.execv -m, so we
no longer build an exec string from repr(sys.argv). Forwards sys.argv[1:]
only: under -m Python sets argv[0] to the module path, so passing the full
argv would re-inject the program name as a spurious Click argument.
Verified: python -m remove_ai_watermarks.cli --version works; test_cli green.
Closes#9
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The CtrlRegen engine module references a non-existent top-level package
'ctrlregen' in four locations:
src/remove_ai_watermarks/noai/ctrlregen/engine.py
L39: from ctrlregen.pipeline import CustomCtrlRegenPipeline
L57: from ctrlregen.color import color_match
L242: from ctrlregen.tiling import resize_center_crop, run_tiled
L267: from ctrlregen.tiling import resize_center_crop
These should be absolute imports of the package's own subpackage. As a
result, the top-level try/except sets _HAS_DIFFUSERS=False and
_HAS_COLOR_MATCHER=False even when the [gpu] extra is correctly
installed, and is_ctrlregen_available() always returns False.
Effect on users: invoking the ctrlregen profile crashes with
ImportError: Failed to auto-install missing dependencies:
controlnet-aux, color-matcher, safetensors
regardless of whether those packages are installed. The auto-install
fallback also fails in uv-managed venvs (uv does not ship pip in the
venv by default), so the error path is unrecoverable.
Reproduction (before fix):
uv sync --all-extras
uv run remove-ai-watermarks invisible <image> --pipeline ctrlregen
# → ImportError as above
Fix: change the four imports to use the package-qualified path
(matching the absolute-import style used elsewhere in the codebase,
e.g. watermark_remover.py).
Verified post-fix on Linux/CUDA (NVIDIA L40S):
- is_ctrlregen_available() returns True
- CtrlRegen pipeline loads, downloads weights, and runs end-to-end
- Tile-based path (image > 512px) processes 6 tiles cleanly
- 142 existing pytest tests still pass
Fixes the uv-secure abort that stopped maintain.sh: idna 3.11 had
GHSA-65pc-fj4g-8rjx (fix in 3.15). uv lock --upgrade-package idna pulls
3.16; uv-secure now reports no vulnerabilities. Lock-only change, 266
tests still pass. Updates the stale CLAUDE.md note.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Deeper re-examination (2026-05-25) of github.com/aloshdenny/reverse-SynthID
on our own data corrects the earlier over-stated dead-end:
- The carrier IS real on solid fills -- measured via per-bin PHASE
COHERENCE (the prior probe used spatial/FFT-magnitude NCC, which can't
see a fixed-phase carrier). White gemini-2.5-flash fills: coherence 0.86
at carriers (0,+/-7..12,20..23) vs 0.31 random; single-image phase-match
+0.83 vs -0.24 for real photos.
- But it does not generalize: carriers are model-version/resolution/color
specific (v4 codebook for 3.1-flash/nb-pro scores ~0.5 on 2.5-flash),
and collapse on real content (coherence ~random; v4 content 0.518 vs
neg 0.504, no separation).
Net: a controlled-fill characterizer, not a real-content detector.
Metadata proxy + visible sparkle + online oracles remain the ceiling.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Collected live samples from three popular generators we lacked:
- Ideogram tags its downloads with EXIF Make="Ideogram AI" (no C2PA, no
SynthID, no imwatermark) -- the Make tag is its only signal. exif_generator
only read Software/Artist/ImageDescription, so it missed this; now reads
Make too. Real cameras put "Apple"/"Canon" in Make (no AI token), so this
stays low-false-positive. 4 originals ingested.
- Recraft (PNG export) and Krea hosting FLUX 2: downloads carry NO detectable
signal -- no C2PA/EXIF/IPTC, and notably no imwatermark despite Krea running
FLUX. identify correctly reports 'unknown'. Both ingested as neg fixtures.
Lesson recorded in CLAUDE.md: the imwatermark detector fires only on pristine
output from a pipeline that runs the encoder (diffusers default, official BFL),
not from re-hosts (Krea/Stability) or re-encoded exports (Recraft/Canva).
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>
Collected live C2PA positives from Bing Image Creator and Stability Brand
Studio (DreamStudio successor) and learned two things our scan got wrong:
- Bing now runs Microsoft's own MAI-Image model, not DALL-E, and signs
C2PA as 'Microsoft'. The scan caught it, but the platform label claimed
'Microsoft Designer (DALL-E / OpenAI backend)'. Relabeled model-neutral:
'Microsoft (Bing Image Creator / Designer)'.
- Stability signs C2PA as 'Stability AI' (cert 'Stability AI Ltd'), which
was not in C2PA_ISSUERS, so it read as 'unknown signer'. Added the issuer
and a platform mapping. Stability uses no SynthID and (on its current
Stable Image model) no imwatermark watermark -- verified, both negative.
Both ingested as SynthID-negative corpus fixtures (they are AI but not
SynthID) for issuer-coverage. Canva skipped: its downloads are re-encoded
design exports that strip C2PA, so a Canva sample would be inconclusive.
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>
scripts/synthid_pixel_probe.py is an experimental/diagnostic tool for the
one pixel-domain question that isn't a dead-end: on solid-color fills the
zero-mean residual IS essentially the watermark carrier. Two modes:
'consistency' (mean pairwise NCC of carriers across fills vs random
baseline) and 'removal' (does the pipeline drop the carrier toward
baseline?). Logic validated synthetically (injected carrier correlates,
random noise doesn't, simulated removal collapses it) -- no real fills or
GPU needed.
Running its metric on the corpus independently re-confirms the documented
dead-end for real content: at matched resolution SynthID positives do not
cluster apart from negatives (within-Gemini 0.07; at 1024 px pos-vs-neg
>= pos-vs-pos). An apparent 0.62 among 1254px ChatGPT positives turned out
to be near-duplicate content (5 renders of one prompt at ~0.92; a distinct
ChatGPT image scored ~0 against them), not a shared carrier. The probe is
solid-fills-only; do not use on real content.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Generic HuggingFace AI-vs-real classifiers are per-generator, degrade
off-distribution, are untested on the metadata-stripped surfaces we
care about (gpt-image, Gemini Nano Banana), and our own SDXL pass would
likely defeat them as it does SynthID. Detection stays local +
signal-based. Decision 2026-05-24.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds 20 tests around the new provenance path:
- identify(): local SD/ComfyUI params -> local-pipeline attribution;
visible-sparkle gating at the 0.5 threshold (mocked detector: above,
below, unavailable, opt-out); metadata verdict not downgraded by a
sparkle hit; OpenAI/SynthID caveats + dedup; ProvenanceReport is
JSON-serializable (the CLI --json path); and the honest edge where a
C2PA manifest without an AI source marker stays 'unknown'.
- CLI 'identify': help, clean PNG, AI PNG platform, valid --json,
missing file.
- gemini_engine.detect_sparkle_confidence: float in range for a real
image, None for an unreadable file.
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>
get_ai_metadata opened the file with PIL unguarded, so a HEIC (or any
format PIL can't open without optional plugins) raised
UnidentifiedImageError instead of falling through to the binary scan --
unlike has_ai_metadata, which already guards. Wrap the open in
except Exception and continue to the C2PA/IPTC path. Regression test
feeds an unopenable .heic shell and asserts no raise.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Grow the SynthID corpus to 109 originals (91 iPhone-photo negatives,
2 positives) and document what was learned studying 8 platforms:
- README: per-platform watermark map (C2PA issuer / SynthID pixel / IPTC
/ visible sparkle per platform) and an "originals, not previews" note
(re-encoded previews strip metadata, so a clean preview is not proof).
- CLAUDE.md: surface-dependent blind spot -- the same Google model wraps
C2PA in the Gemini app but emits the SynthID pixel watermark + sparkle
with no C2PA/IPTC via the API/playground (AI Studio, Nano Banana), so
synthid_source returns None despite SynthID being present; only the
pixel oracle or the visible-sparkle detector catches those.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
PIL cannot open iPhone HEIC without pillow-heif, so width/height stayed
0 for those negatives. Fall back to sips -g pixelWidth/pixelHeight on
macOS when PIL fails; returns (0,0) elsewhere.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
On some manifests (observed: Microsoft Designer) the first CBOR "name"
key precedes a binary hash field, not the generator string, so
_cbor_text_after returns control-char garbage. Guard with isprintable()
to drop it; issuer detection (byte-search) and the SynthID verdict are
unaffected. Adds TestParseChunkGuards covering kept-vs-dropped cases.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds content positives (OpenAI gpt-image: forest, fisherman, tokyo; Google
gemini: fisherman, mug) and SDXL/non-SynthID negatives to the local corpus
manifest. Now spans 4 resolutions across 2 vendors (was solid-black only).
README: documents driving generation via Chrome MCP -- Gemini single-click
download; ChatGPT via in-page fetch+blob (preserves original C2PA bytes,
unlike the flaky UI download / a canvas re-encode).
Images stay gitignored; only the manifest (sha256 + labels + extracted
metadata) and protocol are tracked.
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>
Local SDXL run on a Gemini 3 Pro output (snowboard scene, 2816x1536), seed 42,
strength 0.05, steps 50, ~10 min on MPS. Gemini app's "Verify with SynthID"
returned "no SynthID watermark detected" on the cleaned file. This closes the
verification gap noted in v0.4.0 release notes and confirms architectural
equivalence to the raiw-app production fal-ai/fast-sdxl path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Bump diffusers minimum to 0.38.0 (closes GHSA-98h9-4798-4q5v).
- Refresh uv.lock to pull urllib3 2.7.0 (closes GHSA-qccp-gfcp-xxvc and
GHSA-mf9v-mfxr-j63j via transitive update from requests / huggingface-hub).
- Allow pre-releases globally (`[tool.uv] prerelease = "allow"`) because
diffusers 0.38.0 declares a dependency on safetensors>=0.8.0rc0. Drop
once safetensors 0.8.0 stable is published or diffusers re-pins.
uv-secure --ignore-unfixed now reports zero vulnerabilities.
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>
Reverse alpha blending applied at the assumed default position painted
a visible inverse-sparkle artifact onto clean or edited images. The
function now returns an unmodified copy when detection fails, instead
of falling back to the hardcoded Gemini corner. Bump to 0.3.5.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
test-user