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Victor KuznetsovandClaude Opus 5.5 7ba07869d2 Add paid-service captures, aggregator attribution, and provenance vetoes 
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>
2026-09-26 14:32:53 -07:00

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Documentation

This documentation is split by purpose. Start with the user guides if you want to run the tool. Use the maintainer references only when changing the code.

User guides

Page Use it when
Installation You need the CLI, an optional model backend, or a development environment.
CLI guide You want a command for an image, video metadata or visible marks, a directory, or a specific watermark type.
Python API You want to call the package from Python.
Supported signals You need to know which visible marks, metadata formats, and invisible signals are covered.
Known limitations You need the quality, device, format, or verification boundaries.
Photo pixel classification You want classify / classify_pixels: AI versus camera, optional provider.
Source-pipeline classification You want an abstaining OpenAI/Google/unknown export-pipeline signal after metadata removal.
Photo classifier training You need the retrain pack, public sources, and what is not published.
Scope, safety, and legal notes You need the intended use and legal context.

Maintainer references

Page Purpose
Module internals Current architecture, invariants, and regression guards by module.
Development Environment setup, dependency recovery, CI behavior, and fixture policy.
Watermark benchmark kernel Reproducible local detection, removal-observation, and fidelity measurements.
Code provenance Required notices for licensed derivative work.
Verification plan Verification methods, completed measurements, and remaining validation gaps.
Release and distribution PyPI, Homebrew, Hugging Face Space, photo-classify Hub model, ComfyUI Registry, and the release workflow.
Agent skill Install and maintain the published Agent Skill and Claude plugin.
Watermarking landscape Vendor signals and detection approaches.
SynthID technical reference Mechanism, provenance, robustness, regeneration.

Research archive

These pages record experiments and the evidence behind past decisions. They are not command references and may describe prototypes that were later removed. The current behavior is defined by the code, tests, README, and user guides.

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