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remove-ai-watermarks/CLAUDE.md
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Victor KuznetsovandClaude Opus 5 78d9e81d0f Collapse the duplicated detection path and lift the image pipeline into the library
The visible-mark path had grown three copies of one ladder sweep, four
near-identical `detect` arms, and four hand-rolled `footprint_mask` overrides;
mark knowledge sat in five hand-maintained tables across three modules; and the
flagship `all`/`batch` pipeline existed only in cli.py, written twice with
divergent behavior.

Detection is now one measurement. `_ladder_best` replaces the three sweeps,
`_scan`/`_verdict` replace the four arms, and the winning box travels to the
mask on `TextMarkDetection.match_box` instead of being swept a second time.
`detect_both` returns the strict and relaxed verdicts from one scan, which
halves the arbiter's perception cost (260 -> 130 matchTemplate calls on a 2048²
image, verdicts identical field for field). A per-mark demotion goes in the new
`_post_gate` hook, never in a `detect` override -- an override is invisible to
the single-pass path, which is how the RunningHub and Yuanbao anchor gates
briefly stopped applying.

Everything about a mark is now one registry row: product, label regime, the
platform sentence `identify` reports, the metadata signals that confirm it, and
its TC260 producer codes. `identify._VISIBLE_MARK_PLATFORM`, the signal mapping
in `api.visible_provenance`, `_PRODUCT_OF` and the pill veto are derived from
those rows.

`api.remove_all` / `api.remove_batch` are the library form of the `all` and
`batch` commands; the CLI is a wrapper that owns console text and exit codes.
Progress is a `(stage, detail)` pair of stable tokens, so the CLI keys its
wording off structure rather than parsing the library's prose back.

Two intentional behavior changes, both verified against a recorded 811-image
sample of detector verdicts, removal-mask hashes, arbiter decisions and
`identify` reports:

  * A TC260 label now relaxes the vendor its `ContentProducer` names rather than
    ByteDance's pair on every China-AIGC image. 333 of 811 samples move; on 185
    of them the previously relaxed pair was simply the wrong vendor, and the
    mark actually present never reached the relaxed gate its own
    `provenance_ncc_factor` was calibrated for.
  * A confident LibLibAI detection suppresses the Jimeng pill, like every other
    TC260 product's mark. It was registered alongside RunningHub and Baidu, both
    of which were added to the hand-written veto list, and it was not. 1 sample
    moves, and it is exactly the co-firing case.

Nothing else in that record changes: detector verdicts, mask hashes and
`identify` verdicts are byte-identical, and all 200 calibration constants are
untouched.

Also: `aigc_label` and friends plus `extract_c2pa_info` are memoized on
(path, mtime_ns, size) -- size because this package rewrites in place; the
native TC260 container readers route on magic bytes instead of the file
extension, so a mislabeled AVI or FLV is no longer invisible; `identify` shares
one pixel decode between the DWT-DCT and visible stages (TrustMark keeps its own
Pillow decode, which is not substitutable); and the six `stabilize_*` video
wrappers collapse into one policy table.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-03 22:49:45 -07:00

3.1 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 --region generic 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.

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. Keep the source-distribution public allowlist.

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.

Data safety

Follow data/README.md for public fixture, calibration, oracle, and evaluation layout. 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, and the detection-path measurement rule