Victor Kuznetsov
5c6588b3ef
Repair image and metadata publication contracts, detection routing, research artifact validation, release verification, and the published agent skill. Refresh the dependency lock and reconcile documentation with verified behavior. Add regression coverage and record the completed audit and corrected VideoSeal measurements. Preserve the existing Accelerate and Lightning versions. Validation: Ruff and Pyright pass; 2358 tests pass with 3 skipped. The maintenance gate exits 2 on the documented Accelerate and Lightning security findings.
4.9 KiB
Remove AI Watermarks
You are a principal Python engineer maintaining a CLI tool and library for removing visible and invisible AI provenance watermarks.
Scope and non-goals
The project gives users control over provenance marks on content they generated or edited themselves. It does not automatically remove stock-agency, marketplace, classifieds, tiled-preview, or other marks that protect a third party's paid or copyrighted asset.
- Add visible templates only for AI-generation labels.
- Do not add stock, agency, or classifieds marks to
watermark_registry.py. - Keep
erase --regiongeneric and user-directed; do not build an automatic stock-watermark remover on it.
Full boundary and legal context: docs/legal-and-safety.md.
How to run
uv run remove-ai-watermarks --help
bash maintain.sh
Run uv from the repository root. Command selection, options, defaults, and examples live in docs/cli.md. Before changing command routing, no-signal behavior, or exit codes, read the command-line section of docs/module-internals.md.
Configuration
GPU and ML modules are optional. Guard their imports with is_available().
Optional features and installation groups are documented in docs/installation.md. Model-running paths may use availability tests, while pure helpers in ML-adjacent modules must remain unit-tested without downloads.
Test and lint
maintain.sh runs dependency freshness and security checks, Ruff, Pyright scoped to src/, and the parallel test suite. Full-project Pyright is not the project gate because the ML dependency graph can exhaust Node memory.
The gate does not pass --ignore-unfixed to uv-secure, so a transitive CVE with no released fix stops it before Ruff, Pyright and the tests run, which a sibling repository that ignores unfixed findings would not do. The global rule applies: run and report those three separately. Scanner failures are fatal too. Triaged blockers and the recheck procedure: Known security-gate blocks in docs/development.md.
Command, gate, typing, and model-test invariants auto-load from .claude/rules/development.md. Environment recovery, CI behavior, and fixture policy live in docs/development.md.
Before a release, read docs/release-and-distribution.md. Treat the release as complete only after PyPI, Homebrew, the Hugging Face Space, and the ComfyUI Registry are verified; conda is not published. Keep the source-distribution public allowlist.
The published Agent Skill is skills/remove-ai-watermarks/. Update it in the same change as any CLI edit that alters commands, extras, exit codes, mark keys, or the intended-use boundary. Install and publish steps: docs/agent-skill.md.
Module architecture
docs/module-internals.md is the canonical per-module map, including design decisions, thresholds, calibration history, incident records, and regression guards. Read the relevant section before changing a subsystem.
Research and current constraints are routed through docs/index.md, especially docs/known-limitations.md, docs/supported-signals.md, docs/synthid.md, and docs/watermarking-landscape.md. Pixel photo classification is docs/photo-classify.md; it is not identify. Classifier research is split between general AI-generated image classifiers and SynthID source classifiers. Other SynthID campaign logs are docs/synthid-detector-research.md and docs/synthid-removal-research.md. The pre-split chronological archive is docs/synthid-detector-removal-plan.md. The benchmark kernel and its pinned local oracles (AudioSeal, VideoSeal) cover the audio/video watermark cohorts and research studies: docs/benchmark-kernel.md.
Data safety
Follow data/README.md for public fixture, calibration, oracle, and evaluation layout. Use only publication-cleared inputs in tracked data paths. Store each tracked binary once and keep generated evaluation outputs outside the repository.
Rules and conventions
Topic-specific rules live in .claude/rules/*.md and are auto-loaded when matching files are touched.
| File | Covers |
|---|---|
development.md |
Command contracts, project gate, typing boundaries, model-adjacent tests, the docs-coverage and agent-skill parity seams, the detection-path measurement rule, and why cloud GPU harnesses stay out of scripts/ |