watermarks-remover

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watermarks-remover

Agent skill + stdlib Python scripts to strip multi-vendor AI provenance marks from text and files — for privacy and hygiene on content you own.

Layer	Target	How
A	Invisible Unicode, exotic spaces, bidi, tag chars	Deterministic Python scripts
B	Statistical (token-sampling) text watermarks	Agent rewrite + optional rewrite_text.py hook
Files	C2PA / EXIF / XMP / doc props	PNG, JPEG, WebP, SVG, PDF, DOCX, ODT, HTML, Markdown

Vendors / ecosystems (class-level): Claude, Gemini / SynthID-Text, OpenAI provenance surfaces, open-LLM Kirchenbauer-style marks.

Latest release: v0.4.0

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

Install (agent skill)

# 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 system tools (auto-used when present):

Tool	Role
c2patool	Inspect C2PA manifests
exiftool	Residual metadata strip (esp. PDF)
qpdf	Structural PDF rebuild — required for a real PDF strip (see below)

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

Quick use (scripts)

SCRIPTS=skills/remove-ai-marks/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.

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=skills/remove-ai-marks/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.

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.

Bootstrap

SCRIPTS=skills/remove-ai-marks/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 picks the torch wheel index from the GPU's compute capability rather than the CUDA version nvidia-smi prints — that number is the maximum the driver supports, and drivers are backward compatible, so deriving the wheel tag from it installs cu130 on a Pascal card whose kernels were dropped in cu128. The script forces cu126 below compute capability 7.5 and then verifies the result with torch.cuda.get_arch_list().

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=skills/remove-ai-marks/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

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 subprocess spawned by rewrite_text.py. 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

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=skills/remove-ai-marks/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

Channel	Claude	Gemini/SynthID	OpenAI	Open-LLM
Unicode / edit-based text	Layer A	Layer A	Layer A	Layer A
Statistical sampling text	Layer B best-effort	Layer B best-effort	Layer B if present	Layer B best-effort
C2PA / file metadata	Yes (listed formats)	Yes when present	Yes when present	Yes when present
Pixel image marks	Out of scope	Optional SynthID score + CtrlRegen removal (external); optional MarkDiffusion same-scheme detect + DiffusionPurification removal (external)	Out of scope	Optional CtrlRegen / MarkDiffusion removal (external)
Training backdoors	Out of scope	Out of scope	Out of scope	Out of scope

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

Format	Inspect	Clean
PNG / JPEG / WebP	C2PA chunks / APP11 / RIFF C2PA, AI XMP hints	Drop metadata segments
SVG	<metadata>, XMP	Strip blocks
PDF	Byte/XMP + optional tools	exiftool then qpdf; degraded without either
DOCX	docProps / customXml	Scrub props, drop customXml
ODT	meta.xml	Drop generator / AI-ish meta
HTML	meta, JSON-LD, data-ai*	Strip tags/attrs
Markdown	YAML frontmatter AI keys	Drop keys + Layer A body

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):

Channel	What we remove	What may remain	External check (examples)
Hard-bound C2PA / EXIF / XMP	Yes	Soft-bound / pixel marks	c2patool, Content Credentials verify
SynthID-class media	Optional pixel removal (external CtrlRegen); local score otherwise	Audio/video watermark; residual pixel watermark after removal	Provider tools (e.g. Google SynthID / Vertex detector where offered); optional local reverse-SynthID scorer
Statistical text	Best-effort rewrite	Strong marks after light edit	No public universal detector; vendor tools when available

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

---

Removal options (summary)

Option	Removes	Notes
Unicode scrub (Layer A)	ZWSP, bidi, tags, exotic spaces, …	Safe default for text
Rewrite (Layer B)	Statistical token marks (best-effort)	Always offered by skill; costs style — see Disclaimer
Container/metadata strip	File provenance	See format table
CtrlRegen pixel removal (optional)	Pixel-domain image marks (SynthID-class, StegaStamp, Tree-Ring, StableSignature)	External backend; heavy compute; conservative strength default
DiffusionPurification pixel removal (optional)	Pixel-domain image marks (Tree-Ring-class)	MarkDiffusion backend; blind regeneration (more drift than CtrlRegen); conservative strength default
Open-weight local models	Avoid re-stamping with origin model	Operational alternative

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.

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

• New optional MarkDiffusion image-watermark 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
• Add stdlib-only WebP inspection and metadata cleaning for RIFF C2PA, XMP, EXIF, and ICC profile chunks
• New optional MarkLLM 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 (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
• Mock-based tests (tests/test_markllm_detect.py, 21 cases) — no torch in CI
• Docs: verification-harness caveat (same-config-only, not a vendor-detector oracle) in README, SKILL.md, removal-matrix.md, vendor-notes.md
• 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

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.

References

• 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
• 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)
• 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)