mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-10-14 13:30:55 +02:00
Victor Kuznetsov
8562c457a4
Provider classes now name the GENERATOR that rendered the pixels, never the brand. bytedance joins openai/google/muse-image as the shared ByteDance lineage (Doubao and Jimeng render with one model family; measured: separate heads cross-fire 32/22 at 3.5x mass, the surgical union holds 83.9% on the frozen test cell while openai/google/meta/no_ai stay byte-identical to run1). tc260 stops being a public value: its head remains as the group veto for the rest of China's generators (without it, 207/379 China test rows would be falsely named openai or muse-image), and the loader tolerates provider snapshots that predate bytedance. The receipt gate moves model-side: stable-name artifact receipt-gate.npz carries its threshold, head and threshold always load from one file, and the publish script stages the stable gate from the weights source. Microsoft stays metadata-only: 144 of its 253 bank rows name an OpenAI model as their C2PA softwareAgent and none names a Microsoft renderer. pre-commit: 1) maintain.sh - exit 1 (uv-secure: lightning 2.6.5 via trustmark, PYSEC-2026-3624 no fix released; per docs/development.md the core checks ran separately: ruff clean, pyright 0 errors, 2059 passed 3 skipped); 2) /simplify - single-pass four angles, clean, one noted skip (gate-test npz staging duplication); 3) docs sync - grepped bytedance/receipt-gate.npz/provider values over README, docs, skill, rules: no stale references; 4) CLAUDE.md - improver: no changes, revise: no additions (learnings live in canonical docs)
Maintainer scripts
These scripts are development and evaluation tools, not installed CLI commands. Run
them from the repository root with uv run python scripts/<name>.py --help. Inputs
under .local-eval/ and generated reports remain untracked unless a data README
explicitly names a tracked canonical result.
Audits and release checks
| Script | Purpose |
|---|---|
corpus_gap_scan.py |
Compare a local image corpus with the library's identify results. |
detection_timing.py |
Record per-method metadata and verdict timings. |
detection_timing_report.py |
Aggregate timing records by method and segment. |
fidelity_metrics.py |
Compute objective image-fidelity metrics for paired outputs. |
invisible_quality_audit.py |
Pair originals and invisible-removal outputs for quality review. |
metadata_removal_audit.py |
Check metadata detection/removal parity over a corpus. |
pill_gate_audit.py |
Measure the Jimeng pill detector on the product path. |
real_examples_e2e.py |
Run end-to-end confidence checks over local examples. |
record_parity_audit.py |
Compare record-based and file-based identification. |
resource_ceilings.py |
Measure peak RSS and runtime for fill backends. |
robustness_suite.py |
Exercise CLI failures on adversarial and degenerate inputs. |
sidecar_regression.py |
Compare current identification with recorded sidecars. |
smoke_matrix.py |
Exercise CLI parameter choices on real local data. |
video_fidelity_probe.py |
Compare delivered video fidelity with its source. |
visible_eval.py |
Benchmark registered visible-mark detectors. |
visible_removal_audit.py |
Audit visible-removal results over a local corpus. |
watermark_benchmark.py |
Run hash-pinned image cases while keeping detection, removal observation, and fidelity separate. |
watermark_benchmark_report.py |
Aggregate repeated benchmark results with artifact-aware counts and separate cold/warm timing. |
watermark_benchmark_resources.py |
Measure fresh-process wall time and absolute peak RSS for benchmark manifests. |
Calibration and corpus preparation
| Script | Purpose |
|---|---|
contentseal_transforms.py |
Reproduce and hash-check deterministic Content Seal variants. |
detector_response.py |
Measure detector response over mark size, contrast, background, and aspect. |
fill_quality.py |
Measure visible-fill quality against constructed ground truth. |
ladder_headroom.py |
Measure recall cost from the coarse scale ladder. |
registered_mark_calibrate.py |
Measure a registered detector without conflating visual positives, metadata cohorts, adjudicated negatives, and unlabeled controls. |
synthid_corpus.py |
Ingest and inspect the local SynthID reference corpus. |
vendor_cohort_harvest.py |
Partition TC260 carriers by producer code. |
vendor_mark_calibrate.py |
Calibrate a candidate vendor text detector. |
visible_alpha_solve.py |
Rebuild visible-watermark alpha assets from controlled captures. |
visible_groundtruth.py |
Consolidate blinded contact-sheet labels into ground truth. |
visible_positives.py |
List corpus images carrying a registered visible mark. |
visible_recall_sample.py |
Build an unbiased blinded sample for recall measurement. |
visible_sheets.py |
Build blinded contact sheets for relaxation candidates. |
watermark_benchmark_cohort.py |
Build the deterministic synthetic DWT-DCT and TrustMark cohort consumed by watermark_benchmark.py. |
watermark_benchmark_real_cohort.py |
Build a publication-cleared, provider-paired and content-stratified real-image cohort. |
retrain_photo_classify.py |
CPU-retrain the 2026-08-31 photo heads from a sha256-keyed cache pack, no images. |
publish_photo_classify_hf.py |
Upload the photo-classify card and freeze weights to Hugging Face wiltodelta/raiw-photo-classify. Manual; the Action publish-photo-classify-hf.yml is the write-token path. |
verify_engine_selection_fixtures.py |
Verify hashes, dimensions, and prompt pairing in the tracked auto-engine content matrix. |
Research and diagnostic prototypes
| Script | Purpose |
|---|---|
chroma_text_restoration_study.py |
Compare verified-text restoration for Qwen and Chroma on H100 without running both engines in production. |
cjk_tail_probe.py |
Test a generic template for otherwise uncovered CJK labels. |
controlnet_sweep.py |
Sweep the historical ControlNet removal prototype. |
engine_selection_study.py |
Run one global engine at a time over the tracked prompt-matched content matrix. |
analyze_engine_selection_study.py |
Reduce engine-study output to paired deltas and exact sign tests. |
infer_text_lines.py |
Draft stable source-text lines without modifying pixels. |
qwen_scrub_prototype.py |
Probe low-strength Qwen regeneration on a GPU. |
selective_text_restoration.py |
Evaluate text restoration over a scrubbed image. |
synthid_pixel_probe.py |
Run the experimental local SynthID carrier probe. |
video_synthid_sweep.py |
Build oracle-gated video regeneration candidates. |
Generated assets
| Script | Purpose |
|---|---|
render_pill_silhouette.py |
Render the synthetic Jimeng pill silhouette. |
render_vendor_silhouettes.py |
Render synthetic vendor text-mark silhouettes. |
sync_c2pa_soft_bindings.py |
Validate the official C2PA soft-binding registry and refresh its pinned runtime snapshot. |
maintain.sh and the CI lint job run sync_c2pa_soft_bindings.py --check, so an
upstream registry change fails with the refresh command instead of silently
leaving new algorithms unnamed. The check and runtime parser are read-only;
running the sync command without --check is the explicit snapshot update.
Shared helpers
_plain_console.py provides plain-text fallbacks for Rich output, and
_text_eval.py contains normalization helpers shared by text-evaluation scripts.
engine_selection_manifest.py owns the content-matrix schema and validation
shared by its integrity check and the real-image benchmark builder.