mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-10-14 13:30:55 +02:00
Victor KuznetsovandClaude Sonnet 5
83be3a8926
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>
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 |
Inventory identify, metadata markers, normalized C2PA review fields, unattributed outcomes, errors, and gap candidates over a full or date-bounded corpus. Its schema-versioned checkpoint resumes safely after interruption. |
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. |
provider_oracle_web.py |
Implement isolated Playwright submissions for the unified provider-oracle runner. |
provider_oracle_microsoft.py |
Call Microsoft's Content Provenance API with hash-bound public media and Azure CLI authentication. |
provider_oracles.py |
Plan API-first and drive hash-bound Google, OpenAI, Microsoft, and Meta oracle checks. |
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 the adjudicated registry subset and expose unmeasured marks as scope-zero rows. |
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 and the cleared Qwen symbol fixture. |
visible_groundtruth.py |
Consolidate blinded contact-sheet labels into ground truth. |
visible_positives.py |
Resumably scan registered marks plus metadata cohorts, and build blinded native-detail bands for uncovered and unbiased quiet samples. |
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. |
spectral_pipeline_probe.py |
Measure a prompt-grouped, phase-free spectral source-pipeline hypothesis without presenting it as SynthID detection. |
source_pipeline_classifier.py |
Train or run an abstaining OpenAI/Google/unknown source-export classifier; research-only and not SynthID detection. |
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. |
publish_source_classify_hf.py |
Upload the derivative source-pipeline model and its public card to Hugging Face wiltodelta/raiw-source-classify. Manual; the Action publish-source-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 |
|---|---|
cjk_tail_probe.py |
Test a generic template for otherwise uncovered CJK labels. |
controlnet_sweep.py |
Sweep the historical ControlNet removal prototype. |
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.
hf_publish.py owns the common authenticated, allowlisted Hugging Face model
repository upload used by the two model publishers.
engine_selection_manifest.py owns the content-matrix schema and validation
shared by its integrity check and the real-image benchmark builder.