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>
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>
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>
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>
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>
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>
Condense CLAUDE.md by removing detailed build, test, architecture, release, and pre-commit sections; add a concise 'How to run' example and a brief 'Configuration' heading to surface primary CLI usage and simplify the documentation.
Delete legacy .agents skill docs (get-api-docs, python-code-review) and add configuration and docs for Claude integration. Adds .claude/settings.json to enable WebSearch/WebFetch, register plugins, and run pre/post tool hooks (ruff/pyright auto-checks and auto-fixes). Adds CLAUDE.md with project overview, build/test instructions, architecture, and conventions.
test-user