Files
remove-ai-watermarks/data
Victor KuznetsovandClaude Sonnet 5 83be3a8926 Add generic bare "AI生成" text-mark detector (brand-less TC260 fallback)
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>
2026-09-21 22:01:10 -07:00
..

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)
  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. Keep third-party fixture license notices in licenses/, outside directories that tests enumerate as media inputs.
  7. 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.