Watermarks Remover — strip AI provenance marks

Agent skill wrapping guillaumemeyer/watermarks-remover, a set of stdlib Python scripts that remove multi-vendor AI provenance marks from text (invisible Unicode, statistical sampling watermarks) and files (C2PA / EXIF / XMP / document properties). It is for privacy and hygiene on content you own or are authorized to process — not for academic fraud or falsely claiming "human-written" origin.

When to use this skill

• Stripping invisible Unicode, bidi controls, tag characters, or exotic space homoglyphs that AI tools silently inject into generated text
• Removing C2PA manifests, EXIF/XMP AI-provenance fields, or generator metadata from PNG, JPEG, SVG, PDF, DOCX, ODT, HTML, or Markdown files
• Reducing a statistical (token-sampling) watermark's signal in AI-written prose via a guided rewrite pass, while preserving facts/names/numbers
• Auditing a whole directory tree or a live website (via its sitemap) for AI provenance signals and getting a confidence-classified report
• Optionally scoring or removing a pixel-domain SynthID-class watermark on an image, using the project's external bootstrap scripts

When not to use this skill

• The user wants a guarantee that a vendor's official detector will fail — this toolkit only reports verifiable removals plus best-effort rewrites; no local tool can certify that outcome
• Removing audio/video watermarks or defeating training-data backdoor triggers — explicitly out of scope upstream
• Clearing C2PA soft binding (a watermark that re-links a stripped file to a remote Content Credentials manifest) — stripping hard-bound C2PA metadata does not clear this channel
• The user's stated goal is academic fraud, plagiarism, or a false "human-written" claim about content they don't own — warn per skills/remove-ai-marks/references/ethics.md upstream and only perform technical cleaning on content they are authorized to process

Instructions

Step 1: Clone the repo and resolve the scripts directory

git clone --depth 1 https://github.com/guillaumemeyer/watermarks-remover.git
SCRIPTS=watermarks-remover/skills/remove-ai-marks/scripts

The toolkit is not published as a pip package; it is consumed as a standalone script directory (Python 3.10+ stdlib only). c2patool and exiftool are auto-detected and used when present, especially for PDF.

Step 2: Pick the right layer for the input

Text tools refuse binary input by default (magic-number + control-byte detection) so a .docx/.pdf/image never gets mangled by decoding its compressed bytes as text; pass --force-text to override, or use the _file.py unified tools which route correctly on their own.

Step 3: Inspect before cleaning

python3 "$SCRIPTS/inspect_file.py" --json path/to/input
python3 "$SCRIPTS/inspect_text.py" --json path/or/-
python3 "$SCRIPTS/inspect_image.py" --json image.png

Findings are classified confirmed / probable / informational / likely_false_positive. Summarize suspicious codepoints and C2PA/AI flags before cleaning; do not silently overwrite the original.

Step 4: Deterministic clean (Layer A + file metadata)

python3 "$SCRIPTS/clean_text.py" INPUT -o OUTPUT --stats
python3 "$SCRIPTS/clean_file.py" INPUT -o OUTPUT       # unified, any supported format
python3 "$SCRIPTS/inspect_file.py" OUTPUT               # verify the result

Prefer writing *.cleaned.* outputs unless the user explicitly asked for an in-place edit (--in-place is available but destructive).

Step 5: Always offer Layer B (statistical rewrite) for prose

After Layer A, propose — do not silently skip — a rewrite pass for natural-language content to reduce a token-sampling watermark's signal:

python3 "$SCRIPTS/rewrite_text.py" draft.md --backend print-prompt --strength paraphrase

print-prompt is the CI-safe default (no model call; it just emits the rewrite prompt for the agent to run itself). Local Ollama or an OpenAI-compatible backend can be wired via env vars:

export WATERMARKS_REWRITE_BACKEND=ollama
export WATERMARKS_REWRITE_MODEL=llama3.2
python3 "$SCRIPTS/rewrite_text.py" draft.md -o draft.rewritten.md --strength paraphrase

Rules: API keys come from WATERMARKS_REWRITE_API_KEY only (never on argv); non-loopback endpoints are refused unless --allow-remote or WATERMARKS_REWRITE_ALLOW_REMOTE=1 is set explicitly; prefer a rewrite model that is not the suspected origin vendor, to avoid re-stamping. Always tell the user this step trades prose quality (voice/tone) for hygiene — see the upstream README's "Disclaimer" section — and is best-effort, never a certified bypass.

Step 6: Optional pixel-domain SynthID scoring / CtrlRegen removal

Only reach for these when the input is an image with a suspected pixel-domain watermark (SynthID-class, StegaStamp, Tree-Ring, StableSignature). Both are heavy, external, and not bundled in this repo:

# Detection-only score (external aloshdenny/reverse-SynthID checkout)
"$SCRIPTS/setup_synthid.sh"
REVERSE_SYNTHID_DIR=~/reverse-SynthID ~/reverse-SynthID/.venv/bin/python "$SCRIPTS/score_synthid.py" shot.png

# Removal (external mertizci/noai-watermark checkout; conservative default strength 0.25)
"$SCRIPTS/setup_ctrlregen.sh"
NOAI_WATERMARK_DIR=~/noai-watermark ~/noai-watermark/.venv/bin/python \
  "$SCRIPTS/clean_ctrlregen.py" shot.png -o shot.ctrlregen.png

Expect ~10 GB of model downloads and a strong GPU recommendation for CtrlRegen; CPU runs are slow. clean_image.py --remove-pixel ctrlregen also wires this into the unified image pipeline after a metadata strip. CtrlRegen's backend ships no LICENSE file (treated as all-rights-reserved) and is only ever loaded at runtime from the user's own checkout.

Step 7: Aggregate audits for a tree or a website

python3 "$SCRIPTS/audit_dir.py" DIR --json
python3 "$SCRIPTS/audit_website.py" --sitemap https://example.com/sitemap.xml --json
python3 "$SCRIPTS/audit_website.py" --base https://example.com --json   # auto-discovers the sitemap

audit_website.py is stdlib-only and does not shell out to c2patool/exiftool for remote URLs; download assets and run audit_dir.py locally when that level of detail is needed.

Step 8: Use the wrapper for a read-only environment check

bash .agent-skills/watermarks-remover/scripts/watermarks-remover.sh doctor <repo-dir>
bash .agent-skills/watermarks-remover/scripts/watermarks-remover.sh inspect <repo-dir> <file>
bash .agent-skills/watermarks-remover/scripts/watermarks-remover.sh clean <repo-dir> <file> <output>

doctor only reports Python version and optional-tool availability (c2patool, exiftool); it never writes to disk or installs packages.

Best practices

1. Inspect before you clean, and verify after — inspect_* first, clean_* second, inspect_file.py on the output third.
2. Never overwrite silently — default to *.cleaned.* output paths; only use --in-place on explicit user request.
3. Always offer Layer B, but be honest about its cost — statistical watermark removal means rewording, and rewording degrades voice/tone; surface that trade-off rather than silently rewriting production copy.
4. Prefer a non-origin rewrite model — rewriting Claude text with Claude (or Gemini with Gemini) risks re-stamping the same watermark class into the output.
5. Report confidence honestly — distinguish confirmed/probable findings from informational/likely_false_positive noise, and never claim an official vendor detector will fail.
6. Treat C2PA soft binding and pixel/audio/video marks as out of scope for the deterministic path — only the optional external CtrlRegen backend addresses pixel-domain image marks, and even then only as a best-effort regenerating remover.
7. Keep heavy backends opt-in — don't run setup_synthid.sh / setup_ctrlregen.sh unless the user actually needs pixel-domain scoring/removal; they pull GBs of dependencies and models.

References

• references/commands.md — curated command reference by workflow stage
• scripts/watermarks-remover.sh — read-only doctor + thin inspect/clean wrappers
• Upstream repo: guillaumemeyer/watermarks-remover
• Upstream skill: skills/remove-ai-marks/SKILL.md
• Upstream ethics notes: skills/remove-ai-marks/references/ethics.md
• Project standards: .agent-skills/skill-standardization/SKILL.md

Examples

Example 1: Clean a Markdown draft and verify

git clone --depth 1 https://github.com/guillaumemeyer/watermarks-remover.git
SCRIPTS=watermarks-remover/skills/remove-ai-marks/scripts
python3 "$SCRIPTS/inspect_file.py" --json draft.md
python3 "$SCRIPTS/clean_file.py" draft.md -o draft.cleaned.md
python3 "$SCRIPTS/inspect_file.py" draft.cleaned.md

Example 2: Strip C2PA/EXIF from a screenshot, then check for a pixel watermark

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

Example 3: Environment check before recommending a workflow

bash .agent-skills/watermarks-remover/scripts/watermarks-remover.sh doctor ~/src/watermarks-remover