watermarks-remover

name watermarks-remover description Strip multi-vendor AI provenance marks from text and files using the guillaumemeyer/watermarks-remover Python toolkit: invisible Unicode/space homoglyphs (Layer A, deterministic), statistical token-sampling watermarks via agent-guided rewrite (Layer B, best-effort), and C2PA/EXIF/XMP/doc-props metadata on PNG, JPEG, SVG, PDF, DOCX, ODT, HTML, and Markdown. Covers Claude, Gemini/SynthID-Text, OpenAI provenance surfaces, and open-LLM Kirchenbauer-style marks, plus optional external backends for SynthID pixel scoring and CtrlRegen pixel-domain removal. Use when the user wants to strip AI watermarks, remove C2PA / Content Credentials, clean AI metadata from a file, remove invisible Unicode / zero-width characters from AI-generated text, or audit a directory/website for AI provenance signals. Triggers on: "remove watermark", "strip C2PA", "remove AI metadata", "clean invisible unicode", "remove-ai-marks", "SynthID removal", "audit_dir.py", "Layer A / Layer B watermark removal". allowed-tools Bash Read Write Edit Glob Grep compatibility Python 3.10+ stdlib only for the core inspect/clean scripts (no pip install required beyond the clone). Optional system tools `c2patool` and `exiftool` improve C2PA/PDF handling when present. Optional heavy external backends (SynthID pixel scoring via `aloshdenny/reverse-SynthID`, CtrlRegen pixel removal via `mertizci/noai-watermark`) need their own venvs, ~10 GB of model downloads, and a GPU is strongly recommended for CtrlRegen. MIT license. metadata {"tags":"watermark-removal, ai-provenance, c2pa, synthid, unicode-scrub, metadata-stripping, privacy-hygiene, exif, content-credentials, python","platforms":"Claude, ChatGPT, Gemini, Codex","version":"1.0","source":"https://github.com/guillaumemeyer/watermarks-remover"} 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 Input Path Pasted text / .txt / code Text Layer A ( inspect_text.py / clean_text.py ) .md / .html Container clean (frontmatter/meta) + Layer A .png / .jpg / .jpeg Image metadata strip ( inspect_image.py / clean_image.py ) .svg / .pdf / .docx / .odt Container metadata strip Mixed / unknown Unified inspect_file.py / clean_file.py (auto-routes by format) Directory audit_dir.py for an aggregate report Website / sitemap audit_website.py 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 Inspect before you clean, and verify after — inspect_* first, clean_* second, inspect_file.py on the output third. Never overwrite silently — default to *.cleaned.* output paths; only use --in-place on explicit user request. 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. 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. Report confidence honestly — distinguish confirmed / probable findings from informational / likely_false_positive noise, and never claim an official vendor detector will fail. 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. 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