The agent skill (skills/remove-ai-marks/) is now a code-free remote client:
all implementation moved to service/scripts/ and runs behind a stdlib HTTP
service (server.py) with /health, /capabilities, /inspect, /clean and a
dynamically generated OpenAPI 3.0.3 spec at /openapi.json.
- Move scripts/ and the backend Dockerfiles under service/
- server.py: JSON/base64 HTTP entrypoint with size caps, binary guard,
atomic writes, loopback default, optional bearer auth
- Core Dockerfile (exiftool/qpdf/c2patool preinstalled) and a GHCR publish
workflow for the core/markllm/markdiffusion images
- compose.yaml (wr-* services, harness/heavy profiles) + compose-check.sh
to validate the running stack (exit code only)
- Fix markllm image build (tokenizers 0.22.2, CPU-only torch) and ctrlregen
build (python:3.11 base for the 2023-era research pins)
- Fix markllm/markdiffusion harness images missing common.py at runtime
* feat: optional MarkLLM text-watermark verification harness
Add an optional external backend wrapping THU-BPM/MarkLLM (Apache-2.0)
so a specific statistical text-watermark scheme (KGW / SynthID-Text)
can be verified before/after a Layer B rewrite.
- detect_text_watermark.py: detect/watermark subcommands, external
checkout at a pinned commit, exit codes 0/1/2/3, --json
- rewrite_text.py --markllm-scheme: before/after detection around the
rewrite, reports a `cleared` flag; never fails the rewrite when the
backend is unavailable
- setup_markllm.sh + requirements-markllm.txt (pinned deps) +
Dockerfile.markllm + Makefile bootstrap/smoke/docker targets
- tests/test_markllm_detect.py: 16 mock-based cases (no torch in CI)
- Docs: verification-harness caveat (same-config-only, not a
vendor-detector oracle) in README, SKILL.md, removal-matrix, vendor-notes
* chore: tidy merged Unreleased changelog list
* security: harden the MarkLLM harness (offline, caps, supply-chain)
Addresses the PR security review:
- detect_text_watermark.py: --offline loads the scoring model from the HF
cache only (local_files_only + HF_HUB_OFFLINE, no remote code), and the
algorithm config is capped at 1 MiB so a crafted huge file is refused
before either this script or upstream reads it into memory
- rewrite_text.py: WATERMARKS_MARKLLM_RLIMIT_AS (env, POSIX) optionally
applies RLIMIT_AS to the MarkLLM subprocess; off by default because
torch/CUDA needs large address spaces
- Dockerfile.markllm: drop the unpinned torch install (it is pinned in
requirements-markllm.txt) and verify the cloned upstream commit SHA
- tests: offline flag, config-too-large, and preexec/rlimit cases
- docs: hardening knobs in README + SKILL.md; changelog updated
Wires a standalone clean_ctrlregen.py adapter plus setup_ctrlregen.sh bootstrap, Dockerfile, Makefile targets, and clean_image.py --remove-pixel ctrlregen. The backend is cloned at a pinned commit and never bundled (noai-watermark ships no LICENSE file). Includes mock-based tests and docs with research references.
Add Makefile, pytest.ini, synthetic fixtures (MD/HTML/SVG), PDF degraded
clean test, and make smoke/test targets so the multi-vendor plan is fully closed.
Guillaume Meyer (The Opinionated Man)