remove-ai-watermarks/data

Repository data

Tracked data is organized by purpose:

data/
  fixtures/
    provenance/   Real format and provenance fixtures used by tests
                  (source records live in fixtures/README.md)
    visible/      Per-mark example gallery: one synthetic canonical example per
                  registered mark plus documented provider originals where available
                  (see fixtures/visible/README.md)
    synthetic/    Frozen synthetic renders that unit tests stamp onto frames, each
                  produced by the script named in the test that loads it
  captures/
    2026-09/      Provider outputs from the maintainer's own accounts, stored as
                  served, indexed by manifest.csv (see its README)
  calibration/
    <vendor>/     Minimal controlled inputs needed to rebuild detector assets
  synthid/
    originals/    Canonical provider-oracle fixtures, stored once
    manifest.csv  Provenance and verification record for each original
    full-pipeline-quality.csv
                  Reusable full-pipeline evaluation selection
  evaluations/
    engine-selection/
                  Prompt-matched, publication-cleared AI content matrix for
                  deterministic auto-engine and watermark benchmark research
    fidelity/     Evaluation instructions and hand-verified ground truth
    video-synthid-oracle.csv
                  Reproducible full-clip Gemini SynthID verdicts

image-watermark-examples.csv and video-watermark-examples.csv are the coverage indexes for real carriers. They point to each canonical binary instead of duplicating it and record unsupported gaps and source-specific licensing.

Storage rules

Use only publication-cleared inputs in tracked data paths. Keep generated evaluation outputs outside the repository.

1. Store each binary image once. Evaluation manifests and documentation point to its canonical location.
2. Put executable test fixtures in fixtures/.
3. Put only the minimal reproducible detector inputs in calibration/.
4. Put externally verified SynthID originals in synthid/originals/ and keep both CSV files synchronized.
5. Keep evaluation outputs outside the repository. Record reproducible commands, hashes, and oracle verdicts instead of committing another corpus copy. A small curated before-and-after example may live in docs/images/ when it is part of the public documentation. Local benchmark manifests may point to these canonical inputs or to an untracked corpus; every artifact and reference must carry its SHA-256 digest. evaluations/engine-selection/ is the bounded exception for ORIGINAL study inputs: its manifest tracks a prompt-matched OpenAI/Meta content matrix, not generated model outputs or watermark-oracle evidence.
6. Put provider captures that no test reads in captures/<yyyy-mm>/, with a manifest.csv row per capture. A capture that is also a fixture is stored once, in fixtures/, and its manifest row points there. Request a new capture only for a service x vendor pair not yet captured, or for a service's own model: another version of a known pair adds no provenance evidence.
7. Keep third-party fixture license notices in licenses/, outside directories that tests enumerate as media inputs.
8. Runtime detector assets belong in src/remove_ai_watermarks/assets/. Unregistered research candidates belong in scripts/assets/visible-mark-candidates/ so they are not shipped in the wheel.

The source distribution excludes data/; the wheel contains only package runtime assets.

Video SynthID oracle manifest

evaluations/video-synthid-oracle.csv is the only evidence that the shipped video removal profile works, so it is also the source of truth for three shipped defaults: tests/test_video_invisible.py asserts that noise_std, long_side, and fps together match a row this manifest records as certified. Changing one of those three without adding the row that certifies it fails the suite. Both historical rows mark vae as unrecorded: the repository must not infer a model identity from the current default. Add it to the assertion in the same commit as the first oracle row that records and verifies one.

Column	Meaning
date, source_url, source_sha256	Identify the carrier.
source_width, source_height, source_fps	Carrier geometry. Without it the actual downscale factor of a row cannot be recovered later.
duration_seconds, source_verdict	Clip length submitted and the verifier's reading of the untouched carrier.
vae, noise_std, long_side, fps, seed	The run configuration. unrecorded means the historical run did not preserve the VAE identity.
output_sha256	Identifies the exact submitted file.
output_verdict	One of detected, not_detected, indeterminate, refused.
output_verdict_text	The verifier's wording, verbatim.
output_detected_range	Time range the verifier reported for the output.
track	visual, audio, both, or empty. The verifier scores tracks separately and this path copies source audio unchanged.
session_id	Groups rows submitted in one oracle session, so per-session drift stays visible.
stratum	Content class of the carrier, for stratified certification.
psnr_db, temporal_residual_ratio	Fidelity measurements. Neither is a watermark verdict.

Record indeterminate when the verifier answers with its unclear state rather than a negative: an unclear reading logged as not_detected is exactly the silent regression this manifest exists to prevent. Leave a field empty when it was not recorded, and never backfill it with a plausible value.

psnr_db is measured against the already-resized frame and before the encode, so it excludes the downscale, the decimation, and the codec. Rows stay comparable to each other only while that definition holds.

The two 2026-07-31 rows predate this schema; their empty fields were never recorded and are not recoverable from the row.