test-user
f07ce10c72
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>
SynthID reference corpus
A locally-collected, labeled image corpus for SynthID work. Two downstream uses:
- Per-resolution spectral codebook for an experimental SynthID detector (carrier frequencies are resolution-dependent, so labels must record the exact native resolution).
- Removal regression set — verify that our pipeline turns a SynthID-positive image into a negative one.
There is no reliable local detector of the SynthID pixel watermark (Google's
decoder is proprietary). The ground-truth label therefore comes from an
external oracle, recorded per image in verified_via (see below).
Layout
data/synthid_corpus/
README.md # this protocol (committed)
manifest.csv # labels + provenance (committed, reviewable)
images/ # the actual files (gitignored, local-only)
pos/ # SynthID present
neg/ # SynthID absent
cleaned/ # our pipeline output from a pos image
Images are gitignored on purpose: the corpus is large, may contain personal or
licensed content, and SynthID-positive outputs are best kept local. The
manifest.csv (sha256 + labels + extracted metadata) is the durable artifact.
Verification levels (verified_via)
Ground-truth quality, strongest first:
gemini-app— checked via the Gemini app "Verify with SynthID" feature. Gold standard for the pixel watermark (Google models).openai-verify— checked via openai.com/verify (gold standard for OpenAI ChatGPT/Codex/API images).synthid-portal— checked via Google's SynthID Detector portal.c2pa-metadata— issuer-only proxy (Google/OpenAI C2PA manifest present). Weaker: the C2PA can be stripped while the pixel watermark remains.third-party— label asserted by an external dataset, not independently verified.none— unverified.
Prefer gemini-app for any image that will train the codebook or gate a test.
What to collect
For the codebook (per target resolution, e.g. 1024x1024, 1024x1536, 1536x2816):
- 30-50+ SynthID-positive outputs per resolution (more is better; ~150-200 per resolution materially improves carrier discovery).
- At each target resolution, also a batch of pure-black (#000000) and pure-white (#FFFFFF) fills generated by the SynthID model — these isolate the content-independent carrier (the watermark is most of the signal there).
For the regression set:
- A handful of
posimages, theircleanedcounterparts (run through our pipeline), and the cleaned re-verified viagemini-app(should read negative). negcontrols: non-AI photos and outputs from non-SynthID models (SD, Midjourney, Firefly) verified negative.
Avoid personal or identifiable content; the corpus stays local.
Ingesting
Use scripts/synthid_corpus.py — it copies a file in, records its sha256,
resolution, format, and C2PA issuer (via our own detector), and appends a row
to manifest.csv:
uv run python scripts/synthid_corpus.py ingest path/to/*.png \
--label pos --source "Gemini app" --model gemini-3-pro \
--verified-via gemini-app --notes "1024x1024 batch"
uv run python scripts/synthid_corpus.py status # counts by label / resolution / verification