_ _ _ ____ ___ ____ ____ _  _ ____ ____ _  _ ____    ____ ____ _  _ ____ _  _ ____ ____
| | | |__|  |  |___ |__/ |\/| |__| |__/ |_/  [__  __ |__/ |___ |\/| |  | |  | |___ |__/
|_|_| |  |  |  |___ |  \ |  | |  | |  \ | \_ ___]    |  \ |___ |  | |__|  \/  |___ |  \

watermarks-remover

Agent skill + stdlib Python service to strip multi-vendor AI provenance marks from text and files — for privacy and hygiene on content you own. The skill is a thin client: it drives the machinery over HTTP, so the agent host needs no Python.

Vendors / ecosystems (class-level): Claude, Gemini / SynthID-Text, OpenAI provenance surfaces, open-LLM Kirchenbauer-style (green-list) and keyed-Gumbel / EXP (Aaronson) marks.

Latest release: v0.5.0

Skill path: skills/remove-ai-marks/
Service path: service/
(migration: formerly remove-claude-marks; slash alias /remove-claude-marks still documented)

Install (agent skill)

The skill ships no code — it calls the service over HTTP. Install the skill (markdown only) and start the service, then set WATERMARKS_SERVICE_URL if it is not http://127.0.0.1:8765.

# Grok Build / project-local
mkdir -p .grok/skills
ln -sfn "$(pwd)/skills/remove-ai-marks" .grok/skills/remove-ai-marks

# User-global Grok
mkdir -p ~/.grok/skills
ln -sfn "$(pwd)/skills/remove-ai-marks" ~/.grok/skills/remove-ai-marks

Invoke with /remove-ai-marks or ask to “strip AI watermarks / C2PA / Claude marks / SynthID-class text.”

Optional Cursor text-only skill

skills/clean-user-facing-text/ is a self-contained Cursor skill for authorized manuscripts, documentation, and web copy. It excludes image, C2PA, service, and external-model tooling.

Install it into ~/.cursor/skills/clean-user-facing-text:

python3 install_skill.py

On Windows, use py install_skill.py. The install-skill.sh wrapper is provided for macOS/Linux shells. Existing installations are preserved unless you pass --force; replacement is staged first and the previous install is kept as a uniquely named backup.

Skill invocation is model-selected. Projects that explicitly adopt this workflow can also copy the optional rule:

mkdir -p /path/to/project/.cursor/rules
cp integrations/cursor/clean-user-facing-text.mdc \
  /path/to/project/.cursor/rules/clean-user-facing-text.mdc

For all projects, put the same instruction in Cursor User Rules instead. Rules improve consistency but remain model instructions; Cursor does not expose a deterministic pre-send filter for final chat responses.

Start the service

The fastest path is a local HTTP server (Python 3.10+ stdlib only — no deps, no Docker):

make serve                 # http://127.0.0.1:8765
# or directly:
python3 service/scripts/server.py --host 127.0.0.1 --port 8765

Windows (no Docker)

See docs/windows-autostart.md for auto-starting the service at Windows login without Docker.

For the whole infra (core + optional harness/heavy backends), see Docker / compose below.

Optional system tools (auto-used when present — preinstalled in the core Docker image):

Core scripts need Python 3.10+ stdlib only. Layer B model calls are optional.

Quick use (scripts)

SCRIPTS=service/scripts

# Unified inspect / clean
python3 "$SCRIPTS/inspect_file.py" draft.md
python3 "$SCRIPTS/clean_file.py" draft.md -o draft.cleaned.md
python3 "$SCRIPTS/clean_file.py" photo.png -o photo.cleaned.png
python3 "$SCRIPTS/clean_file.py" notes.docx -o notes.cleaned.docx

# Text Layer A
python3 "$SCRIPTS/inspect_text.py" draft.md
python3 "$SCRIPTS/clean_text.py" draft.md -o draft.cleaned.md --stats

# Layer B rewrite hook (default: print prompt only — no model required)
python3 "$SCRIPTS/rewrite_text.py" draft.md --backend print-prompt --strength paraphrase
# Optional local Ollama (loopback only by default — remote endpoints require
# WATERMARKS_REWRITE_ALLOW_REMOTE=1 or --allow-remote):
# WATERMARKS_REWRITE_BACKEND=ollama WATERMARKS_REWRITE_MODEL=llama3.2 \
#   python3 "$SCRIPTS/rewrite_text.py" draft.md -o draft.rewritten.md
# API keys are read from WATERMARKS_REWRITE_API_KEY only (never argv).

# Images
python3 "$SCRIPTS/inspect_image.py" shot.png
python3 "$SCRIPTS/clean_image.py" shot.png -o shot.cleaned.png

Text tools refuse binary input

inspect_text.py, clean_text.py and rewrite_text.py operate on text. Pointed at a .docx, .pdf or image they used to decode the compressed bytes and report whatever codepoints fell out — noise that tracks the compression, not the content — and clean_text.py then wrote those mangled bytes back, destroying the file. They now refuse binary input and name the tool that handles it:

python3 "$SCRIPTS/inspect_text.py" report.docx
# refusing to treat report.docx as text: it looks like a ZIP container (DOCX, ODT, …).
# Use inspect_file.py / clean_file.py, which route by format,
# or pass --force-text to scan the raw bytes anyway.

Detection is by magic number plus a control-byte ratio, so text in encodings other than UTF-8 keeps working. --force-text overrides it everywhere.

Unrecognized formats are never auto-cleaned

classify() labels bytes that match no supported text, image or container format as unknown — it no longer falls back to "text". In auto mode clean_file.py refuses such files (exit 2, no output written) instead of decoding them as UTF-8 and writing back mangled bytes; --as text or --force-text are the explicit opt-ins. inspect_file.py reports the file as unknown (exit 0), and the HTTP service answers /inspect with kind: "unknown" but rejects /clean of unknown formats (400 — send a filename with a known extension, e.g. notes.txt).

HTTP service

The same machinery runs as a stdlib HTTP service (service/scripts/server.py) — the interface the skill uses and the way any web app can integrate without vendoring:

Batch endpoints loop the same per-file pipeline as /inspect and /clean, capped at WATERMARKS_MAX_BATCH_FILES files per request (default 50). A malformed entry (bad base64, unknown option, unrecognized format) surfaces as that entry's "ok": false with an "error" string — it never aborts the rest of the batch.

WM="http://127.0.0.1:8765"
curl -s "$WM/health"                       # {"ok": true, "version": "..."}
curl -s "$WM/openapi.json"                 # machine-readable OpenAPI 3.0.3 contract
curl -s -X POST "$WM/clean" -H 'Content-Type: application/json' \
  -d "{\"file\": \"$(base64 < notes.md | tr -d '\n')\", \"name\": \"notes.md\"}"

The service routes by filename extension then magic bytes, so text / image / container are auto-detected. Set WATERMARKS_SERVER_API_KEY to require Authorization: Bearer <key> on every request. Loopback-only bind by default (--host to override); intended for a trusted network.

Watermark detection (/detect and detect_before / detect_after)

Detection is a separate step from cleaning — the service never calls vendor APIs unless you ask it to:

• POST /detect runs the configured watermark detectors on a file. Text → vendor detectors + stylometry; image → SynthID pixel score.
• /inspect accepts an opt-in "detect": true flag that appends detector results to the text report (and can flip suspicious).
• /clean accepts "detect_before" / "detect_after" options to score the input and the cleaned output, so you can measure what a clean actually changed.

Text detectors (see /capabilities → text_detectors):

Image scoring: when WATERMARKS_SYNTHID_SCORER_URL is set, the service scores images through the wr-synthid-score sidecar (heavy profile); with a local REVERSE_SYNTHID_DIR it uses the checkout directly. Detection is fail-soft: unconfigured, timed-out, or errored detectors report {"available": false, "error": ...} and never block cleaning.

Docker / compose

Published images (GHCR):

Build and run the core service:

make docker-core-build
docker run --rm -p 127.0.0.1:8765:8765 --read-only --tmpfs /tmp watermarks-remover
# any CLI stays runnable by overriding the command:
docker run --rm -v "$(pwd):/data" watermarks-remover \
  /app/scripts/clean_file.py /data/notes.md -o /data/notes.cleaned.md

Whole-infra bring-up:

docker compose up -d                         # core HTTP service only
docker compose --profile harness up -d       # + markllm / markdiffusion
docker compose --profile heavy up -d         # + ctrlregen / synthid (local builds)
docker compose --profile harness --profile heavy up -d   # all services

The compose stack maps the core service to 127.0.0.1:8765. The harness/heavy services are one-shot CLIs — invoke with docker compose run --rm <service> … when you need verification or pixel work.

Validate the running stack (exit code only, no output on success):

make compose-check        # or: ./compose-check.sh

Checks wr-core via GET /health and runs each harness/heavy service with --help, requiring exit 0.

Configuration (env vars for docker compose)

Nothing is required to clean arbitrary text — the core service works out of the box:

echo "Hello\u200bWorld\u00ad!" > /tmp/sample.txt
curl -s -X POST http://127.0.0.1:8765/clean -H 'Content-Type: application/json' \
  -d "{\"file\": \"$(base64 < /tmp/sample.txt | tr -d '\n')\", \"name\": \"sample.txt\"}"

Everything else is optional and lives in a .env file at the repo root. docker compose auto-loads .env and interpolates the ${VAR} references in compose.yaml from it (shell exports win over .env if both are set).

cp .env.example .env       # then edit
docker compose up -d       # picks up .env automatically

.env is gitignored (deny-by-default) — never commit it. For host-side CLI runs (rewrite_text.py, the skill), export the same file into the environment:

set -a; . ./.env; set +a; python3 service/scripts/rewrite_text.py /tmp/x.txt -o /tmp/x.rewritten.txt

Layer B is agent-orchestrated in the skill (it rewrites with its own model), so the WATERMARKS_REWRITE_* vars are only needed when driving rewrite_text.py directly.

Images publish automatically on v* tags via .github/workflows/release-images.yml.

Optional SynthID pixel scoring

inspect_image.py and clean_image.py can report a pixel-domain SynthID confidence score when an external checkout of aloshdenny/reverse-SynthID is available. The scorer is not bundled: it is loaded at runtime from your checkout, and its code remains under the upstream project's non-commercial Research License.

Option 1: one-command bootstrap (no Docker)

SCRIPTS=service/scripts

# Clones upstream, creates a venv, and installs scorer-only dependencies.
"$SCRIPTS/setup_synthid.sh"

# Score an image (default checkout: ~/reverse-SynthID).
REVERSE_SYNTHID_DIR=~/reverse-SynthID \
~/reverse-SynthID/.venv/bin/python "$SCRIPTS/score_synthid.py" shot.png

# Or surface the score from inspect / clean (same venv Python).
REVERSE_SYNTHID_DIR=~/reverse-SynthID \
~/reverse-SynthID/.venv/bin/python "$SCRIPTS/inspect_image.py" shot.png

setup_synthid.sh accepts --dir PATH, --ref REF, and --full (install the full upstream requirements.txt, which adds torch/diffusers for the upstream VAE bypass this project does not use).

On Windows use setup_synthid.ps1 (-Dir, -Ref, -Full), which creates the venv at .venv\Scripts\ — the layout image_meta.py already looks for on os.name == "nt".

Option 2: local Docker build

make docker-synthid-build
# Run unprivileged and with a read-only rootfs; the scorer only needs to read
# /data and write to stdout/tmp.
docker run --rm \
  --user "$(id -u):$(id -g)" \
  --read-only --tmpfs /tmp \
  -v "$(pwd):/data" \
  watermarks-remover-synthid-scorer /data/shot.png

The image is built locally from the upstream source at build time. It is not published, so it does not redistribute the upstream code.

Option 3: HTTP scorer sidecar (docker compose)

Under the heavy profile the compose stack also runs the scorer as an HTTP sidecar (wr-synthid-score) so the published core service can score images before/after cleaning without bundling the non-commercial upstream code. Point wr-core at it and share a bearer key (see .env.example):

# .env
WATERMARKS_SYNTHID_SCORER_URL=http://wr-synthid-score:8766
WATERMARKS_SYNTHID_SCORER_API_KEY=change-me

docker compose --profile heavy up -d

Then POST /clean with {"options": {"detect_before": true, "detect_after": true}} returns synthid_before / synthid_after in the report, and POST /detect on an image returns the SynthID score. Fail-soft: if the sidecar is down or unconfigured, reports carry {"available": false, "error": ...} and cleaning still succeeds.

V4 scoring uses artifacts/spectral_codebook_v4.npz from the upstream checkout (`220 MB). This is detection/scoring only — it does not remove pixel watermarks.

Optional CtrlRegen pixel removal

For pixel-domain image watermarks (SynthID-class, StegaStamp, Tree-Ring, StableSignature), an optional external backend runs the CtrlRegen pipeline (ControlNet + DINOv2 IP-Adapter controllable regeneration). The backend is mertizci/noai-watermark, a maintained reimplementation of the ICLR 2025 CtrlRegen method with automatic tiling.

The backend is not bundled and ships no LICENSE file, so it is treated as all-rights-reserved: it is cloned at a pinned commit and loaded at runtime. Its research-era dependency pins (requirements-ctrlregen.txt — e.g. transformers==4.37.2, diffusers==0.27.2) carry published advisories and are intentionally not current, so they are only ever installed inside the dedicated venv this script creates and never into the main service image; setup_ctrlregen.sh also re-verifies the pinned commit on existing checkouts, not just fresh clones.

Bootstrap

SCRIPTS=service/scripts

# Clones upstream (pinned commit), creates a venv, installs torch + deps.
"$SCRIPTS/setup_ctrlregen.sh"

# Standalone removal (default checkout: ~/noai-watermark).
NOAI_WATERMARK_DIR=~/noai-watermark \
~/noai-watermark/.venv/bin/python "$SCRIPTS/clean_ctrlregen.py" shot.png -o shot.ctrlregen.png

On Windows use setup_ctrlregen.ps1 (same flags as -Dir, -Ref, -Python); the venv lands in .venv\Scripts\, which clean_image.py already resolves. It probes the published PyTorch wheel indices and picks the highest one at or below the CUDA version nvidia-smi prints that actually exists — that number is the maximum the driver supports, and drivers are backward compatible, so a driver reporting 13.1 (no published cu131) installs cu130. Below compute capability 7.5 it forces cu126, the last index whose wheels still carry Maxwell/Pascal/Volta kernels. It installs torch and torchvision together from that index so the dependency install cannot swap them for CPU builds from PyPI, then verifies after install that torch.cuda.is_available() is true — if a GPU was detected but torch ends up CPU-only, the script warns loudly and exits non-zero instead of pretending the setup succeeded.

From clean_image.py

NOAI_WATERMARK_DIR=~/noai-watermark \
~/noai-watermark/.venv/bin/python "$SCRIPTS/clean_image.py" shot.png \
  -o shot.cleaned.png --remove-pixel ctrlregen

Order of operations: metadata strip first, then CtrlRegen pixel removal, then an optional reverse-SynthID before/after score (when REVERSE_SYNTHID_DIR is also set).

Strength is conservative by default (--ctrlregen-strength 0.25), because higher strength removes more watermark but regenerates more of the image. Documented presets: 0.15 minimal / 0.25 default / 0.35 balanced / 0.5 aggressive / 0.7 max (backend default is 0.5). --ctrlregen-steps defaults to 50 (effective denoising steps ≈ steps × strength).

Image size (512×512 native limit)

CtrlRegen is a 512×512 Stable Diffusion 1.5 ControlNet. The backend resolves this for arbitrary inputs, so no extra tiling is exposed here:

• ≤512 px: single pass — center-crop/resize to 512, regenerate, resize back.
• >512 px: automatic overlapping tiling (512 px tiles, 192 px overlap), width/height aligned to multiples of 8, then cosine-blended seams.
• Either path: output is resized to the original size and color-matched to the original image.

Very large images (e.g. 4K) produce many tiles, so runs scale with tile count (slower and higher VRAM). Pre-downscale large inputs when practical; tile size and overlap are hardcoded upstream and are not exposed as flags.

Compute, gated models, and verification

Expect ~10 GB of model downloads; a GPU is strongly recommended and CPU runs are slow. Some upstream models are gated, so export HF_TOKEN (env only — never argv). clean_ctrlregen.py refuses to auto-install dependencies; run setup_ctrlregen.sh first.

There is no local detector for StegaStamp/Tree-Ring/StableSignature, so the only local signal is the reverse-SynthID score (a surrogate). When available, clean_image.py --remove-pixel ctrlregen reports that score before/after; the official Google SynthID check remains the final authority.

Docker

make docker-ctrlregen-build
docker run --rm -e HF_TOKEN="$HF_TOKEN" \
  --user "$(id -u):$(id -g)" \
  -v "$(pwd):/data" \
  watermarks-remover-ctrlregen /data/shot.png -o /data/shot.ctrlregen.png

Optional MarkLLM text-watermark verification

For controlled experiments, an optional external harness wraps THU-BPM/MarkLLM (Apache-2.0) to watermark test text and re-detect it after a Layer B rewrite — e.g. prove that a KGW (Kirchenbauer, your "open-LLM" row) or SynthID-Text (Gemini row) mark disappears under your rewrite. It is a verification harness, not an oracle: MarkLLM detection is only valid against the same scheme config + keys used at generation, and it cannot certify a vendor detector will fail.

The backend is not bundled. setup_markllm.sh clones upstream at a pinned commit, creates a venv, and installs pinned deps (torch + transformers); the scoring model (default facebook/opt-1.3b, Apache-2.0) downloads from Hugging Face on first run.

SCRIPTS=service/scripts

# Bootstrap (clones upstream, creates ~/MarkLLM/.venv, installs deps).
"$SCRIPTS/setup_markllm.sh"

# Generate watermarked + unwatermarked sample text under the KGW scheme.
MARKLLM_DIR=~/MarkLLM \
  ~/MarkLLM/.venv/bin/python "$SCRIPTS/detect_text_watermark.py" watermark prompt.txt \
    --scheme kgw -o wm.txt -o2 plain.txt

# Detect the scheme mark in a text file.
MARKLLM_DIR=~/MarkLLM \
  ~/MarkLLM/.venv/bin/python "$SCRIPTS/detect_text_watermark.py" detect wm.txt --scheme kgw --json

Verification around a Layer B rewrite: pass --markllm-scheme to rewrite_text.py (with --markllm-dir), and it records the MarkLLM detection before/after plus a cleared flag:

export WATERMARKS_REWRITE_BACKEND=ollama WATERMARKS_REWRITE_MODEL=llama3.2
MARKLLM_DIR=~/MarkLLM \
  python3 "$SCRIPTS/rewrite_text.py" wm.txt -o wm.rewritten.txt \
    --markllm-scheme kgw --markllm-dir "$HOME/MarkLLM" --json-stats

Detection-guided iterative rewriting: Layer B now rewrites iteratively and stops as soon as an attempt passes evaluation. Each evaluation round generates --candidates variants (default 1, WATERMARKS_REWRITE_CANDIDATES) and --max-loops caps how many rounds run before the best-effort variant is returned (default 1, WATERMARKS_REWRITE_LOOPS). Each variant is one rewrite call plus one evaluation, and a round exits early on the first attempt the evaluator reports as not watermarked — so raising --max-loops retries new variants until an evaluation passes (a typical clean rewrite costs one attempt). The evaluator is chosen by priority:

1. MarkLLM — same-config research detection, when --markllm-scheme is passed (with --markllm-dir). A vendor-detector slot is reserved above MarkLLM for Google's SynthID-text detector, which Google retired on its API in Aug 2026 — a future vendor endpoint can plug in there.
2. bigram-Jaccard lexical divergence — when no detector is configured; no pass/fail verdict, so every attempt is generated and the most lexically diverged one is selected (the original behavior).

--json-stats reports the evaluator, attempts made, pass/fail, and per-attempt records:

{
  "evaluator": "markllm",
  "candidates": 1,
  "max_loops": 2,
  "attempts_made": 2,
  "passed": true,
  "candidate_scores": [
    {
      "lexical_divergence": 0.91,
      "selection_score": 0.91,
      "selected": false,
      "passed": false,
      "evaluation": {"detector": "markllm", "available": true, "scheme": "kgw",
                     "is_watermarked": true, "score": 4.3, "threshold": 3.0}
    },
    {
      "lexical_divergence": 0.84,
      "selection_score": 0.84,
      "selected": true,
      "passed": true,
      "evaluation": {"detector": "markllm", "available": true, "scheme": "kgw",
                     "is_watermarked": false, "score": 1.7, "threshold": 3.0}
    }
  ],
  "markllm": {"scheme": "kgw", "before": {"...": "..."}, "after": {"...": "..."},
              "cleared": true, "note": "same-config only"}
}

A detector that is unconfigured, times out, or errors yields an "available": false entry with an error reason and never fails the rewrite — that attempt simply cannot pass, and the loop falls back to lexical-divergence selection. When the max is exhausted without a pass, the least-watermarked (lowest score) attempt is returned as best-effort with a note.

If the backend is unconfigured or its deps are missing, the rewrite proceeds and the report notes verification was unavailable. A GPU is recommended; CPU runs work but are slow, and the model download is a few GB.

Hardening knobs:

• --offline on the adapter (or any MarkLLM run) loads the scoring model from the Hugging Face cache only — zero network egress; fails fast if not cached. Custom remote code is never executed (transformers trust_remote_code is never enabled).
• WATERMARKS_MARKLLM_RLIMIT_AS=<bytes> (env, POSIX) applies an address-space limit to the MarkLLM detector subprocess. Off by default because torch/CUDA usually needs large address spaces.
• Config files are capped at 1 MiB; the upstream checkout and the base image are pinned by SHA/digest.

Docker

make docker-markllm-build
docker run --rm --user "$(id -u):$(id -g)" -v "$(pwd):/data" \
  watermarks-remover-markllm detect /data/wm.txt --scheme kgw --json

Keyed-Gumbel (Aaronson EXP) same-key verification

ARBI's technical report describes the keyed-Gumbel ("exponential") text watermark — now shipping in the open-source arbi-serve engine (ARBI_WATERMARK_KEY) — where the sampler's noise is derived from a keyed hash of the last 4-token context window. Detection is a model-free replay: recompute u = PRF(Hash(key, window), token) from the text alone and test the Gamma tail, so it needs no GPU, model, or logits. This repo ships that detector as detect_gumbel.py (stdlib-only; the p-value is the exact Poisson-sum identity for an integer Gamma shape):

# Text mode (deterministic word/run tokenizer) — quick checks and rewrite-loop
# evaluation; exact replay against a real engine needs its tokenizer:
python3 service/scripts/detect_gumbel.py draft.txt --key 0x... --json

# Exact replay: pass the engine's token ids (JSON array or one per line).
python3 service/scripts/detect_gumbel.py ids.json --tokens --key 0x... --json

Same honesty caveat as MarkLLM: this is a same-key replay — valid only against the same key, tokenizer, and PRF layout used at generation, and a negative result establishes nothing. The HMAC-SHA256 layout here is an auditable instantiation, not bit-compatible with any specific engine kernel (see the module docstring for what to adapt for exact replay).

Detection-guided rewriting: pass --gumbel-key to rewrite_text.py (env: WATERMARKS_GUMBEL_KEY, preferred) and the iterative rewrite loop is driven by the same-key Gumbel replay — evaluator priority becomes gumbel > MarkLLM > lexical divergence — with a gumbel.before/after/cleared report:

export WATERMARKS_REWRITE_BACKEND=ollama WATERMARKS_REWRITE_MODEL=llama3.2
export WATERMARKS_GUMBEL_KEY=0x...
python3 "$SCRIPTS/rewrite_text.py" wm.txt -o wm.rewritten.txt --json-stats

The key never appears in stats or logs. Self-hosted operators who hold their engine's key can verify a rewrite cleared a Gumbel mark; everyone else treats Layer B as best-effort only.

Optional SynthID-text removal benchmark

bench_synthid_text.py measures how effectively a Layer B rewrite clears SynthID-text-class watermarks and at what cost. It generates watermarked + unwatermarked samples with the MarkLLM SynthID scheme (same-config detection, sanity-gated), runs your rewrite variants (strength × max rewrite attempts; the loop stops early on pass) plus controls (no-removal, Layer-A-only, optional re-stamp check), and writes a shareable report.md / results.json / results.csv. Full guide: docs/synthid-text-benchmark.md.

Requires a MarkLLM checkout (setup_markllm.sh / MARKLLM_DIR) and a rewrite backend. The rewriting model is an LLM you configure — the same rewrite_text.py backend the skill uses. MarkLLM's default facebook/opt-1.3b (--markllm-model) is only the watermark generator/detector; it never rewrites. Configure the rewrite model via env vars or benchmark flags (they mirror the config table above):

# Ollama (loopback):
python3 service/scripts/bench_synthid_text.py --markllm-dir ~/MarkLLM \
  --rewrite-backend ollama --rewrite-model llama3.2

# OpenAI-compatible API (remote):
WATERMARKS_REWRITE_API_KEY=... python3 service/scripts/bench_synthid_text.py \
  --markllm-dir ~/MarkLLM --rewrite-backend openai-compatible \
  --rewrite-model deepseek-v4-flash --rewrite-base-url https://api.deepseek.com \
  --rewrite-allow-remote

Use a non-origin model for rewriting (do not rewrite with the same watermarked model that generated the text) or the rewrite can re-stamp the output; --restamp-control measures this.

Optional MarkDiffusion image-watermark harness

For controlled experiments on images, an optional external harness wraps THU-BPM/MarkDiffusion (Apache-2.0), a generative watermarking toolkit for latent diffusion models (it embeds marks — it does not remove them). We use it for three things:

1. Verification harness (like MarkLLM, but for images): watermark a test image with a scheme, run removal, and re-detect with the same scheme config — e.g. prove a Tree-Ring-class mark clears under your pipeline. It is a verification harness, not an oracle: detection requires the generating model (and keys for key-based schemes), so it cannot certify a vendor detector will fail on an arbitrary image.
2. Optional pixel-removal engine: its DiffusionPurification regeneration attack is exposed as clean_image.py --remove-pixel diffusion, an alternative to CtrlRegen. It is blind regeneration (no ControlNet conditioning), so it drifts image content more than CtrlRegen — conservative strength default (0.3), treated as a fallback/comparison, never a guarantee.
3. Local same-scheme detector for Tree-Ring-class marks, partially filling the "no local detector for StegaStamp/Tree-Ring/StableSignature" gap (it covers Tree-Ring/Ring-ID/Gaussian-Shading etc., not StegaStamp / StableSignature / SynthID-media).

The backend is not bundled. setup_markdiffusion.sh creates a venv and installs markdiffusion==1.0.2 from PyPI (pinned), with torch installed from the right platform index; --checkout installs an editable clone at a pinned commit instead. The Stable Diffusion model (default huanzi05/stable-diffusion-2-1-base) downloads from Hugging Face on first run.

SCRIPTS=service/scripts

# Bootstrap (PyPI pin default; creates ~/markdiffusion/.venv, installs deps).
"$SCRIPTS/setup_markdiffusion.sh"

# 1. Generate a Tree-Ring watermarked image (+ unwatermarked control).
echo "a red fox in snow" > /tmp/prompt.txt
MARKDIFFUSION_DIR=~/markdiffusion \
  ~/markdiffusion/.venv/bin/python "$SCRIPTS/markdiffusion_harness.py" watermark \
    /tmp/prompt.txt -o wm.png -o2 plain.png --scheme tr --json

# 2. Remove with the DiffusionPurification regeneration attack.
MARKDIFFUSION_DIR=~/markdiffusion \
  ~/markdiffusion/.venv/bin/python "$SCRIPTS/markdiffusion_harness.py" purify \
    wm.png -o wm.purified.png --purification-strength 0.3 --json

# 3. Re-detect with the SAME scheme config.
MARKDIFFUSION_DIR=~/markdiffusion \
  ~/markdiffusion/.venv/bin/python "$SCRIPTS/markdiffusion_harness.py" detect \
    wm.purified.png --scheme tr --detector-type l1_distance --json

Or run purification as part of the normal image pipeline:

MARKDIFFUSION_DIR=~/markdiffusion \
  ~/markdiffusion/.venv/bin/python "$SCRIPTS/clean_image.py" shot.png \
    -o shot.cleaned.png --remove-pixel diffusion

Hardening knobs mirror the MarkLLM harness: --offline loads the model from the Hugging Face cache only (zero network egress, no remote code), HF_TOKEN is env-only (never argv), algorithm configs are capped at 1 MiB, and the subprocess gets the same higher resource caps as CtrlRegen.

Docker

make docker-markdiffusion-build
docker run --rm --user "$(id -u):$(id -g)" -v "$(pwd):/data" \
  watermarks-remover-markdiffusion detect /data/wm.png --scheme tr --json

The image installs a CPU torch; CUDA users should run setup_markdiffusion.sh on the host instead. Model downloads still hit the HF hub on first run.

Coverage matrix

Details: skills/remove-ai-marks/references/vendor-notes.md, mark-classes.md.

How text marking works (short)

Modern LLM watermarks often hide a signal in which tokens are chosen (generative / sampling bias), not only in invisible characters. Edit-based schemes inject Unicode or synonym rules. File schemes attach C2PA or generator metadata.

• Layer A removes edit-based Unicode carriers (testable).
• Layer B attacks sampling watermarks via heavy rewrite (best-effort; literature-standard attacks such as paraphrase / back-translation).
• File cleaners strip C2PA/XMP/props from supported containers.

Until vendors ship public detectors and keys, no tool can honestly certify “this fails the official check.” Reports must separate verifiable vs best-effort work.

Prefer a non-origin model for Layer B (do not rewrite Claude text with Claude if you are trying to avoid re-stamping).

Disclaimer: what removing a text watermark costs

Text watermarks live in the wording itself: the signal is spread across token choices, so nearly every sentence carries a little of it. Two consequences follow, and they are why Layer B is honestly described as best-effort rather than a magic eraser.

1. Removal means rewording, not restructuring. Shuffling paragraphs, changing headings, or light touch-ups barely move the signal. Stripping a statistical mark requires rewriting a substantial fraction of the text — sentence by sentence, not section by section.

2. Rewording degrades the copy. Any rewrite replaces the original word choices with the rewriting model's, which flattens tone, voice, and precision. On production copy (SEO, marketing, client work) that degradation is real and often visible to the people who care most about the writing. It is like taking text from a top-tier model and asking a less capable model to rewrite it from scratch: the result cannot exceed the rewrite model's ceiling.

Which leads to the honest full-circle question:

If the plan is to rewrite the text with a cheaper model anyway, why pay for a premium model in the first place? Generating directly with the cheaper model is simpler, cheaper, and produces the same — or better — end result.

Layer B makes sense when you specifically want the premium model's thinking and drafting and accept a rewrite pass to satisfy a hygiene or privacy requirement — not as a cheap route to mark-free text.

When to skip Layer B:

• Quality matters more than hygiene: use the lossless path — Layer A Unicode scrub plus the file metadata cleaners — and keep the original prose.
• Rewriting anyway: use a non-origin model (rewriting with the origin model can re-stamp the text), and remember residual risk remains — no tool can certify a vendor detector will fail.

File formats

Why PDF needs qpdf, not just exiftool

ExifTool writes PDFs incrementally. exiftool -all= appends a %BeginExifToolUpdate block that frees the Info object and drops /Info from the trailer — but the original metadata bytes stay in the file verbatim, and exiftool itself can undo the edit with -PDF-update:all=. The command exits 0, viewers show no metadata, and the file gets larger, which is the tell.

For a provenance-stripping tool that is a silent leak, so clean_pdf follows the exiftool pass with qpdf --linearize, which re-serializes the document from its object graph and drops the now-unreferenced objects. Without qpdf installed the clean still runs, but it says so:

warning: exiftool PDF edits are incremental — the original metadata bytes
remain recoverable; install qpdf for a structural rewrite

Pixel-domain watermark removal is now available as an optional external CtrlRegen backend (see above); it is a regenerating remover, not a guarantee. C2PA soft binding (in-content watermark that can re-link a remote Content Credentials manifest after metadata is stripped) remains out of scope. Stripping hard-bound C2PA does not clear those channels.

Residual risk after a clean

This tool reports verifiable removals (Unicode counts, metadata actions) and best-effort Layer B rewrites. It cannot certify that vendor detectors will fail.

To check residual signals yourself (optional, external):

Industry two-layer context (C2PA + imperceptible watermark): Institute of AI PM guide.

Removal options (summary)

Matrix: skills/remove-ai-marks/references/removal-matrix.md.

Ethics and disclaimer

See skills/remove-ai-marks/references/ethics.md. For privacy and research on your content — not academic fraud or false “human-written” claims.

Responsible use: This project is for content you own or are authorized to process. Users must adhere to local regulations and use it responsibly. The developers disclaim any liability for potential misuse by users.

Ecosystem

Third-party projects that wrap or complement this repository, listed for discoverability only. They are not maintained, endorsed, or supported by this project. This project does not review their code, vouch for their behavior or guarantees, or take responsibility for anything you install or run from this list. Each project is governed by its own license, maintainers, and documentation — read those before using it.

MetaClean — desktop GUI

MetaClean is an independent MIT-licensed Rust/Tauri desktop application (Windows, macOS, Linux) providing a packaged native GUI for drag-and-drop metadata cleaning, with a system tray and Explorer integration. It is a separate codebase: it does not call this repository's Python service, and its supported formats and cleaning guarantees differ from this project's. See its README for details.

unmark-web — browser web UI

unmark-web is an independent, MIT-licensed static web client. It removes invisible Unicode marks from text and strips provenance metadata from images entirely in the browser, and can optionally call this repository's HTTP service for the formats it does not handle locally. It is a separate codebase and is not affiliated with this project; see its README for scope and limits.

Adding a project

To register a project here, open a PR adding a short entry — project name, what it wraps or adds, and a link to its own repository. Keep entries brief and factual; do not claim compatibility with, or endorsement by, this project. Please avoid names that start with or closely resemble watermarks-remover — look-alike names make it hard to tell which project is which.

Pre-commit hook

CI gating already exists (audit_dir.py's SARIF export, see Coverage matrix context) — the pre-commit hooks below catch the same class of problem earlier, before a marked file is even committed. Both wrap the existing CLIs (audit_dir.py / clean_file.py) — no separate detection logic.

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/guillaumemeyer/watermarks-remover
    rev: v0.5.0   # pin to a tag/commit
    hooks:
      - id: watermarks-remover-check   # fails the commit if marks are found
      # - id: watermarks-remover-clean # opt-in: cleans staged files in place instead

watermarks-remover-check fails the commit and lists findings; watermarks-remover-clean is opt-in and rewrites staged files in place (exits 1 so you review the diff and re-stage — the same convention as auto-fixing hooks like ruff --fix). When the cleaner cannot process a file at all — it crashed, was killed, or produced no report — watermarks-remover-clean names that file and exits 3 instead, so a cleaner that failed is never mistaken for an already-clean file. Run either by hand with python3 service/scripts/check_staged.py <files...> / clean_staged.py <files...>.

Tests

python3 -m venv .venv && .venv/bin/pip install pytest
.venv/bin/python -m pytest          # or: make test
make smoke                          # quick CLI smoke on fixtures

Changelog

Unreleased

• Strip reserved Default_Ignorable code points in Layer A: U+2065, U+FFF0–U+FFF8, U+E0000, U+E0080–U+E00FF, and U+E01F0–U+E0FFF are unassigned code points carrying Other_Default_Ignorable_Code_Point=Yes, so conformant renderers display them invisibly, normalisation preserves them, and category-based (Cf) scrubbing never sees them: ideal covert carriers with no legitimate use in interchange text. Layer A now strips them and inspect reports them under the new reserved_ignorable kind. Applied to both the service engine and the vendored lightweight-skill copy
• Fix Layer A missing three invisible Default_Ignorable carriers: U+180F (Mongolian free variation selector-4, added in Unicode 14), U+3164 (Hangul filler), and U+FFA0 (halfwidth Hangul filler) are blank-rendering Default_Ignorable code points, but their Unicode categories (Mn/Lo) meant the Cf catch-all never saw them and they were absent from the strip set — so both inspect_text and clean_text passed them through untouched even between plain ASCII. They are now stripped and flagged like their already-covered siblings (U+180B–U+180D, U+115F/U+1160), with the same in-context preservation: U+180F is kept after a Mongolian letter exactly like FVS1–3, and U+3164/U+FFA0 are kept after a Hangul jamo of their own presentation form (compatibility jamo U+3131–U+318E, halfwidth jamo U+FFA1–U+FFDC) exactly like the conjoining fillers, so partial-syllable text is not corrupted. Applied to both the service engine and the vendored lightweight-skill copy
• Strip Unicode noncharacters in Layer A: the 66 noncharacters (U+FDD0–U+FDEF plus U+FFFE/U+FFFF at the end of every plane) are permanently reserved for internal use and prohibited in interchange text, render as nothing or tofu, and survive normalisation, yet both inspect_text and clean_text passed them through untouched: a ready-made covert channel. Layer A now strips them and inspect reports them under the new noncharacter kind. Unlike other reserved ranges they can never be assigned, so stripping carries no future-Unicode risk. Applied to both the service engine and the vendored lightweight-skill copy
• Stop stripping visible-layout format controls next to their own script: Egyptian hieroglyph quadrat controls (U+13430–U+1343F), Duployan shorthand controls (U+1BCA0–U+1BCA3), and musical beam/tie/slur/phrase controls (U+1D173–U+1D17A) are category Cf, so the catch-all stripped them, yet they visibly govern how their script renders (quadrat stacking, shorthand overlaps, beaming): removing them changes the rendered text, contradicting the "cleaners preserve the document body" invariant. They are now preserved when adjacent to their own script, exactly like the existing Mongolian/Khmer/Hangul handling, and still stripped (and flagged) when floating between unrelated text; --strip-emoji-glue paranoid mode still strips them everywhere. Applied to both the service engine and the vendored lightweight-skill copy
• Layer B rewriting is now iterative and evaluation-driven: each round generates --candidates variants (default 1, WATERMARKS_REWRITE_CANDIDATES) and --max-loops (default 1, WATERMARKS_REWRITE_LOOPS) caps the evaluation rounds, stopping as soon as an attempt passes watermark detection. Evaluator priority: MarkLLM (when --markllm-scheme) > bigram-Jaccard lexical divergence (fallback; a vendor-detector seam is reserved for a future SynthID-text endpoint).
• rewrite_text.py --json-stats now reports evaluator / max_loops / attempts_made / passed and per-attempt candidate_scores records (loop, passed, evaluation); markllm.before/after/cleared is unchanged.
• SynthID-text benchmark: default variants paraphrase:3; report and CSV now carry attempts per document (mean_attempts, att column; attempts / evaluator / passed columns); --rewrite-loops mirrors --max-loops.
• Keyed-Gumbel (Aaronson EXP) same-key verification: new stdlib-only detect_gumbel.py implements the model-free replay test of ARBI's keyed-Gumbel report (u = PRF(Hash(key, window), token); exact Gamma-tail p-value; repeated- window masking) — no GPU, model, or logits. rewrite_text.py --gumbel-key (env WATERMARKS_GUMBEL_KEY, preferred) makes it the iterative-loop evaluator (priority: gumbel > markllm > lexical divergence) with a gumbel.before/after/ cleared report; the detector is also exposed as gumbel in /capabilities and /detect. Same-key-only: valid against the same key, tokenizer, and PRF layout used at generation — not a vendor oracle. The key is never logged.

v0.5.0 — service & Docker distribution, HTTP API, and verification harnesses

Service / Docker distribution

• Skill/service split: the skill (skills/remove-ai-marks/) is now a code-free remote client over HTTP; all implementation moved to service/scripts/ and runs behind server.py, a stdlib HTTP entrypoint (/health, /inspect, /clean, /capabilities)
• HTTP service: service/scripts/server.py exposes the cleaning pipeline over JSON/base64; hardening mirrors the CLIs (size caps, binary guard, atomic writes, loopback default, optional WATERMARKS_SERVER_API_KEY bearer auth)
• OpenAPI: GET /openapi.json serves a dynamically generated OpenAPI 3.0.3 spec (built from the route table + live config, so it never drifts from the real endpoints); CI validates it with openapi-spec-validator
• Core Docker image (service/Dockerfile): full cleaning service with exiftool / qpdf / c2patool preinstalled; any CLI stays runnable by overriding the command
• Docker / compose: compose.yaml brings up the whole infra (core always; markllm / markdiffusion behind profile: harness; ctrlregen / synthid behind profile: heavy as local-only builds); services are prefixed wr-; harness/heavy services default to command: ["--help"] so docker compose up --profile harness --profile heavy exits cleanly (one-shot CLIs are run with docker compose run); new make compose-check / compose-check.sh validates the running stack (exit code only)
• GHCR publishing: .github/workflows/release-images.yml publishes core, markllm, markdiffusion images on v* tags; ctrlregen / synthid are never published (upstream licensing)
• Env configuration: .env.example + service configuration guide; docker compose auto-loads .env; .env is gitignored (deny-by-default)
• Repo hygiene: .gitignore and service/.dockerignore are now deny-by-default — only explicitly allowed paths can be committed or sent in a build context (image contexts only ship service/scripts/, which is all the Dockerfiles COPY)
• Tests: tests/test_http_server.py (13 cases) for the HTTP service; all suites re-pointed at service/scripts/

MarkDiffusion image-watermark harness (optional)

• New optional harness (external THU-BPM/MarkDiffusion, Apache-2.0): markdiffusion_harness.py with watermark / detect / purify subcommands for nine image schemes (Tree-Ring, Ring-ID, ROBIN, WIND, SFW, Gaussian-Shading, GaussMarker, PRC, SEAL)
• clean_image.py --remove-pixel diffusion runs the MarkDiffusion DiffusionPurification regeneration attack as an alternative pixel-removal engine (conservative strength 0.3 default)
• setup_markdiffusion.sh bootstrap (PyPI pin 1.0.2; --checkout editable clone at pinned commit) + requirements-markdiffusion.txt + Dockerfile.markdiffusion and Makefile bootstrap-markdiffusion / smoke-markdiffusion / docker-markdiffusion-build / docker-markdiffusion-help
• Mock-based tests (tests/test_markdiffusion_harness.py) — no torch in CI; references/markdiffusion.md reference doc
• Docs: same-scheme-only verification caveat (not a vendor-detector oracle) and blind-regeneration drift caveat in README, SKILL.md, removal-matrix.md, markdiffusion.md

MarkLLM text-watermark harness (optional)

• New optional harness (external THU-BPM/MarkLLM checkout, Apache-2.0): detect_text_watermark.py with detect / watermark subcommands for KGW and SynthID schemes
• rewrite_text.py --markllm-scheme runs before/after detection around a Layer B rewrite and per-candidate detection when --candidates N>1 (env-gated; reports cleared)
• setup_markllm.sh bootstrap + requirements-markllm.txt (pinned deps) + Dockerfile.markllm and Makefile bootstrap-markllm / smoke-markllm / docker-markllm-build / docker-markllm-help
• Hardening: --offline cache-only model loading (no HF egress, no remote code), 1 MiB config cap, optional WATERMARKS_MARKLLM_RLIMIT_AS on the rewrite subprocess, pinned torch in the Dockerfile, and clone-SHA verification in Dockerfile.markllm
• Mock-based tests (tests/test_markllm_detect.py, 21 cases) — no torch in CI; verification-harness caveat (same-config-only, not a vendor-detector oracle) documented in README, SKILL.md, removal-matrix.md, vendor-notes.md

Fixes and polish

• Layer B: rewrite_text.py now sends reasoning_effort: "none" by default for openai-compatible backends (--reasoning-effort / WATERMARKS_REWRITE_REASONING_EFFORT; off omits it). Reasoning models like deepseek-v4-flash otherwise burn ~100s of chain-of-thought on a one-line rewrite (9,894 vs 12 completion tokens)
• Fix markllm image build: requirements-markllm.txt pinned tokenizers==0.23.1, which conflicts with transformers==5.15.0 (caps tokenizers<=0.23.0; no 0.23.0 release exists) — now pinned tokenizers==0.22.2; torch moved to the CPU wheel index (torch==2.13.0.*) so the image is CPU-only like Dockerfile.markdiffusion
• Fix ctrlregen image build: the 2023-era research pins (safetensors==0.4.3, transformers==4.37.2 → tokenizers<0.19) ship no Python 3.14 wheels, so the base image is now python:3.11-slim (digest-pinned, multi-arch)
• Fix harness images at runtime: Dockerfile.markllm and Dockerfile.markdiffusion never copied common.py into /app (pre-existing bug) — added
• WebP: stdlib-only inspection and metadata cleaning for RIFF C2PA, XMP, EXIF, and ICC profile chunks (#37)
• BMP / GIF / TIFF: stdlib-only detection, inspection, and metadata cleaning — GIF comment/XMP extensions are dropped while NETSCAPE2.0 looping is preserved; TIFF IFD metadata (XMP/EXIF/GPS/IPTC/MakerNote) is dropped with payloads zeroed and strip offsets kept, for both classic and BigTIFF; BMP trailing metadata is truncated with the file-size field rewritten
• EPUB: stdlib-only container cleaning — OPF metadata and XHTML meta/JSON-LD scrubbed, embedded raster/SVG media stripped, Layer A applied to XHTML body text, marker-carrying metadata parts dropped, and OCF-encrypted parts passed through untouched
• Filename sanitization: HTTP service refuses unsafe client-supplied output names
• Fix markdown frontmatter cleaner crashing on and leaking nested AI keys (#25)
• Text tools refuse binary input; --force-text overrides (#24)
• --json no longer suppresses the residual-signal exit code (#30)
• inspect_file prints the filename in its output (#50)
• Preserve mixed-case CMS generator meta tags (#42)
• Preserve load-bearing script invisibles, strip PUA in Layer A (#38, #52)
• Preserve script joiners, flag emoji, and Arabic Cf marks in Layer A (#28)
• Harden website audit against SSRF and gzip bombs (#49)
• SECURITY.md only references the private advisories channel (#51)
• Windows: PowerShell ports of the setup bootstraps (#40)
• Docs: add stars/forks shields and drop star-history chart; add MarkLLM to README references; pull request template; plan for Docker CLI + API deployment

v0.4.0 — pixel removal, finding confidence, Windows & false-positive fixes

Optional CtrlRegen pixel removal (external backend)

• Optional pixel-domain watermark removal via an external mertizci/noai-watermark checkout: clean_ctrlregen.py adapter + setup_ctrlregen.sh bootstrap (pinned commit, sparse checkout, venv, SHA verification), plus Dockerfile.ctrlregen and make bootstrap-ctrlregen / docker-ctrlregen-build / smoke-ctrlregen
• clean_image.py --remove-pixel ctrlregen runs metadata strip → CtrlRegen removal → optional reverse-SynthID before/after score; inspect_image.py hints at the flag on a high SynthID score
• Conservative default strength 0.25 (presets 0.15/0.25/0.35/0.5/0.7); the 512×512-native pipeline is auto-tiled by the backend for larger images; the torch subprocess gets higher env-overridable resource caps
• Backend is never bundled: noai-watermark ships no LICENSE file (treated as all-rights-reserved), and its auto-install/restart code paths are bypassed by using CtrlRegenEngine directly

Finding confidence and aggregate audits

• Findings are now classified confirmed / probable / informational / likely_false_positive, exposed in text/image/container JSON and human reports
• New audit_dir.py (recursive tree) and audit_website.py (sitemap discovery + crawl) aggregate reports; documented in SKILL.md

False-positive fixes

• DOCX: scan only docProps/customXml, not the visible body (#14)
• Text Layer A: preserve emoji VS16/ZWJ after an emoji base; new --strip-emoji-glue paranoid flag (#22)
• HTML: treat CMS generator tags as informational, not AI metadata (#13)
• PDF: exclude stream payloads from the AI-marker byte scan (#13)
• Inspect reports note unsupported/best-effort paths

Windows support

• Gate POSIX-only preexec_fn and os.fchmod so writes and optional tools run on Windows (#15, #23)
• Reconfigure stdio to UTF-8 so redirected Windows streams no longer raise on invisible Unicode; Windows CI leg + CLI smoke run (#23)

Docs and supply chain

• README CtrlRegen section + research references (CtrlRegen, UnMarker, forensic-stealth caveat), responsible-use disclaimer; SKILL/matrix/vendor-notes/ethics updates
• Dependabot config + security-path CODEOWNERS; bump scipy/numpy/opencv-python/scikit-learn/pywavelets and the base image to Python 3.14-slim
• Mock-based CtrlRegen tests (no torch in CI)

v0.3.2 — security hardening (safe writes, HTTP client, CI supply chain)

• Safe, atomic output writes: every cleaner now writes via temp-file + atomic rename (safe_write_bytes / safe_write_text), refuses symlinked destinations, and creates .bak backups through the same safe path — pre-placed symlinks (e.g. in /tmp or download dirs) can no longer redirect a clean write onto an arbitrary file
• rewrite_text.py HTTP client hardening: redirects are refused outright, so an API key in the Authorization header can never be re-sent to an unvalidated host; non-loopback endpoints are denied by default (opt in with --allow-remote or WATERMARKS_REWRITE_ALLOW_REMOTE=1); only http(s) schemes are accepted; --api-key was removed — keys are env-only via WATERMARKS_REWRITE_API_KEY
• Resource caps: default max input 1 GiB → 256 MiB, new 64 MiB stdin cap, DOCX/ODT zip budget 512 MiB → 128 MiB, and RLIMIT_AS/RLIMIT_FSIZE applied to exiftool/c2patool/SynthID subprocesses (all caps env-overridable)
• Supply chain: CI actions SHA-pinned with permissions: contents: read, pinned dev deps (requirements-dev.txt), a pip-audit step, and a new CodeQL workflow; the Docker image now runs as an unprivileged user with pip pinned
• Scorer deps: Pillow bumped 10.4.0 → 12.3.0 (24 known CVEs); API usage verified against the pinned upstream commit
• Tests: 18 new security regression tests (60 total, all passing)

v0.3.1 — stronger Layer B statistical-watermark rewrite

• rewrite_text.py default paraphrase now performs an explicit word-choice + syntax attack (clause order, connectors, transition words, sentence boundaries, function words) rather than a generic rewrite
• New --strength humanize: zero-shot "write like a human" pass targeting formulaic AI-style phrasing
• New --strength code: rewrites comments, docstrings, and string literals, and renames local identifiers while preserving behavior and public API names
• Structural pass now emits "natural, varied human prose" instead of AI-typical "clear professional style"
• New --temperature (default 0.9) for both Ollama and OpenAI-compatible backends
• New --candidates N: generates N rewrites and selects the most lexically diverged (bigram Jaccard distance) with a length-drift guard
• Stronger model hygiene: prefer local open-weight models and avoid any known-watermarked vendor, not just the suspected origin
• Residual-risk reporting now distinguishes short/highly predictable text (lower risk) from long, high-entropy prose (higher risk)
• Docs updated in SKILL.md, removal-matrix.md, and vendor-notes.md; tests cover new prompts, divergence scoring, and candidate selection

v0.3.0 — optional SynthID pixel scoring

• Optional pixel-domain SynthID scorer via an external aloshdenny/reverse-SynthID checkout (score_synthid.py); surfaced in inspect_image.py / clean_image.py with REVERSE_SYNTHID_DIR or --synthid-dir
• setup_synthid.sh bootstrap (scorer-only dependencies; --full installs upstream requirements); Dockerfile.synthid plus make docker-synthid-build / docker-synthid-help
• Makefile smoke-synthid and bootstrap-synthid targets
• Tests for the scorer adapter, CLI unavailable path, JSON parsing, and runtime errors
• Docs: detection/scoring only (no pixel removal); upstream code is not bundled and remains under its non-commercial Research License

v0.2.0 — c2patool false-positive fix

• image_meta.py: has_manifest no longer flags Error: No claim found / No JUMBF data found as a manifest (operator-precedence bug: the negative markers now veto every positive branch)
• New tests/test_c2patool_report.py (4 cases: no claim, no JUMBF, genuine manifest, tool absent)
• Docs: fixed c2patool links (repo moved to contentauth/c2pa-rs); added a disclaimer on the quality cost of text-watermark removal

v0.1.0 — packaging polish + provenance honesty

• Makefile (test / smoke / install-skill) and pytest.ini
• Fixture samples for Markdown, HTML, SVG; PDF degraded-clean test
• Docs: industry two-layer model (hard-bound C2PA vs soft binding / SynthID-media)
• README residual-risk table + links to external verify tools
• Reference: Institute of AI PM C2PA/SynthID guide
• Soft-binding and pixel/audio/video watermarks explicitly out of scope in skill/matrix/ethics

v0.0.1 — initial multi-vendor release

• Agent skill remove-ai-marks (replaces Claude-only remove-claude-marks)
• Layer A: invisible Unicode / bidi / tag chars / space homoglyphs (inspect_text / clean_text)
• Layer B: rewrite guidance + optional rewrite_text.py (print-prompt, Ollama, OpenAI-compatible)
• Files: C2PA/AI metadata strip for PNG, JPEG, SVG, PDF, DOCX, ODT, HTML, Markdown
• Unified inspect_file.py / clean_file.py
• Multi-vendor docs (Claude, Gemini/SynthID-class, OpenAI, open-LLM)
• Stdlib-first scripts; optional c2patool / exiftool

License

MIT — see LICENSE.

Bibliography

• How Claude marks AI-generated content (Anthropic)
• Dathathri et al., Scalable watermarking for identifying large language model outputs (SynthID-Text, Nature 2024)
• Google AI for Developers, SynthID safeguards (Gemini API docs)
• C2PA / c2patool
• Kirchenbauer et al., A Watermark for Large Language Models
• Evseev, D. (Arbitration City), Accurate, Costless, and Invisible AI Text Watermarking for Self-Hosted AI Inference (technical report, August 2026) — keyed-Gumbel watermarking shipped in the open-source arbi-serve engine, with exact-test detection and speculative-decoding support — PDF
• THU-BPM/MarkLLM (unified toolkit for evaluating LLM watermarking algorithms)
• Pan et al., MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models (JMLR) — the embedding toolkit this repo's optional image-watermark harness wraps — code, docs
• Zhang et al., Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models (ICML 2024)
• Sander et al., Watermarking Makes Language Models Radioactive — watermarks survive fine-tuning and mark downstream models trained on watermarked data
• Pan et al., Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? — watermark-based provenance and protection against knowledge distillation
• google-deepmind/synthid-text (research reference; not used for detection here)
• aloshdenny/reverse-SynthID (research reference)
• Liu et al., Image Watermarks are Removable Using Controllable Regeneration from Clean Noise (ICLR 2025) — the pixel-regeneration method the optional CtrlRegen backend implements — code
• Kassis & Hengartner, UnMarker: A Universal Attack on Defensive Image Watermarking (arXiv:2405.08363; IEEE S&P 2025) — a universal watermark attack compared on a different metric than CtrlRegen
• Goonatilake & Ateniese, Removing the Watermark Is Not Enough: Forensic Stealth in Generative-AI Watermark Removal (arXiv:2605.09203) — motivates the conservative-strength default: removal can still leave forensic traces
• mertizci/noai-watermark (CLI/Python toolkit for SynthID/StableSignature/TreeRing removal and AI metadata stripping)
• 0xROOTPLS/DeSynth (SynthID removal for OpenAI/Google images)
• Institute of AI PM, AI Content Provenance and Watermarking: The PM's Guide to C2PA and SynthID (two-layer industry model: C2PA + imperceptible watermark / soft binding; SB 942 / EU AI Act Art. 50 context)