Nova Canvas images published in aws-samples were reported as Canva: with no
Amazon row, the byte fallback matched the Canva token inside the "Nova
Canvas" software agent. Add an Amazon Web Services row ahead of Canva, which
also names Nova Reel video.
When the reader cannot open a manifest, scan for signers in the C2PA store
the container locates (JPEG APP11, PNG caBX) instead of the whole metadata
region. A vivo X300 camera capture was labeled "C2PA signer: Adobe" from its
XMP toolkit string. The SynthID provenance scan reads the same store.
Adds the Nova Canvas and Nova Reel samples as MIT-0 fixtures and records the
public-sample search for the other conformance-list signers.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Collect provider samples from Higgsfield, Runway and Dreamina plus Google
Photos, Gemini, Grok and YouTube re-encodes into data/captures/2026-09 with
a hash-checked manifest, and use them to fix attribution and false positives:
- Attribute aggregator outputs: Higgsfield job metadata, Dreamina's
re-signed manifests, Runway's own C2PA signer, TC260 video producers.
- Veto any visible video mark when a valid C2PA claim names another vendor
(a Gemini Omni clip read as Sora on steam), and gate Kling and Hailuo
matches on edge, anchor and white-label evidence.
- Veto the Gemini sparkle when trusted provenance names another vendor.
- Report SynthID as present for Google Photos AI edits: Google's checker
found it on all four measured edits, though manifests record no action.
- Add Vidu video and Wan image marks, TC260 audio placements, and record
the AIGC key in metadata records.
- Document the research sweep, the Dreamina badge decision, and verified
Google statements on SynthID in Photos.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The OpenArt visible-mark engine (commit 9d29d2c) was registered without
a data/fixtures/visible/openart/example.png gallery canary and without
an _ALTERNATE_SIZE entry in test_visible_fill_quality.py, breaking
TestGallery::test_every_registered_mark_has_an_example,
TestGallery::test_engine_detects_its_own_example[openart], and
test_cv2_mask_quality_smoke[alternate-openart] on every CI platform.
Generate the fixture via render_visible_examples.py (self-detects at
conf=0.968) and add the missing size entry.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Every existing text-mark engine (Doubao, Jimeng, Qwen, Baidu, Kling,
Yuanbao) is tuned to one vendor's exact wordmark silhouette, so none of
them match a bare "AI生成" strip with no brand name in front of it.
Confirmed on raiw-app production: a free `visible` run left a bottom-right
"AI生成" watermark completely unremoved because no detector fired.
Research found the wording is not tied to one dominant generator: vivo's
own support documentation confirms its Gallery stamps a bare "AI生成"
label after AI消除/AI扩图/大片模式 editing, and independent reporting
documents the same watermark on Xiaomi Gallery's AI-edit output. Since
the label is a GB 45438-2025/TC260 compliance stamp rather than one
vendor's brand mark, this adds one generic fallback (`generic_ai_label`)
instead of another per-OEM tuned config.
The detection silhouette is font-rendered (no logo element to capture)
via the shared scripts/render_vendor_silhouettes.py pipeline already
used for Qwen/Baidu/Kling/Yuanbao's own text-only marks. Calibration is
synthetic-only (no real captured generator screenshot was available);
the module docstring and DETECT_NCC_THRESHOLD comment carry that caveat
explicitly and this should be recalibrated once real captures exist.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The Content Seal manifest named the production GPU worker in its provenance
column and in two row keys, several docstrings described their sources as user
uploads from the private corpus, and that corpus was called by the storage
product holding it. Each is now what a public reader can check: a pipeline and
strength, material that is not publication-cleared, and a private corpus. The
corpus test follows the renamed rows. Deployment and tier detail goes the same
way, since which backend each hosting tier can afford is a product decision,
not a library fact.
Twelve harnesses that need a cloud GPU account leave scripts/. The research
pages that cite their findings name them as harnesses kept outside this
repository and stop implying a reader can rerun them; the tracked inputs and
recorded verdicts remain the durable record, and the offline analysis step
stays here with its test. A development invariant states the rule so the next
harness is not added back.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
A throwaway test CA with leaf subject CN=Ideogram, Inc signs a flat PNG
carrying a trainedAlgorithmicMedia claim. The fixture exercises the
issuer-to-platform attribution end to end through the real reader and pins
the second dimension the project separates: attribution names Ideogram
while signer trust stays untrusted. Recipe documented in the fixtures
README; no keys, vendor bytes, or user uploads are committed.
detect_doubao_frame reuses the image engine's synthetic doubao alpha as its
video template, so the glyph cannot drift between registries. The policy is
seedance-style single-floor, calibrated by sequential decode over 39
TC260-confirmed Doubao corpus videos and 25 negatives: a 12-frame stable run
at 0.35 accepts 33/39 positives and 0/25 negatives (0.30 already accepted a
negative), and through the shipped arbiter identify_video selects 32/39 with
no false selection. Also documents the measured impossibility of a bare-pill
removal arm with the current detector (68 OCR-confirmed pills max 0.317 vs
clean negatives max 0.353 -- no separating threshold exists).
One committed example per registered mark: 12 PNG (image registry) and 6 MP4
clips (video registry), generated by scripts/render_visible_examples.py from
the committed silhouettes and detector templates -- never from user uploads.
The generator self-verifies (exit 1 when a mark misses its own example) and
tests/test_visible_examples.py holds both sides to it: registry completeness
both ways, per-engine detection on the canonical example, and the shipped
temporal selection accepting each clip.
Second tranche of measured-but-unregistered candidates parked under
scripts/assets/visible-mark-candidates/ with a README recording why none
ships yet (positives do not separate from clean negatives): samsung_en,
gemini_text, notebooklm, dola, mindvideo, higgsfield, jianying, capcut, zsky,
chromastudio, digenai, gendo.
