Watermark Cleaner

Remove AI text artifacts, hidden unicode characters and file metadata before you publish.

Watermark Cleaner is a deterministic, offline, zero-dependency tool for content you own. It cleans the mechanical traces that mark text as machine generated, flags the stylistic phrases that read as AI writing, and strips identifying metadata from images without touching a single pixel. It ships as a Python CLI and a Node CLI that share one rule set and are tested to behave identically.

See it

The problem is invisible by definition. Escaped, it looks like this:

before   "Sm​art quotes “work” — and a hidden‌ watermark"
after    "Smart quotes \"work\", and a hidden watermark"

$ watermark-cleaner check post.md
post.md
  characters   fixed  removed zero width space (U+200B)          x2
  typography   fixed  straightened smart quotes                  x2
  voice        error  ai phrases present (rewrite required)      x1

1 file checked, 1 blocked
$ echo $?
1

check reports and never changes anything. fix cleans in place and writes a .bak backup next to every changed file.

Install

Both packages install a single command, watermark-cleaner, available in every folder, like git.

Use

watermark-cleaner check .          # report only, changes nothing, exits non zero if blocked
watermark-cleaner fix .            # clean in place, writes a .bak backup per changed file
watermark-cleaner check post.md
watermark-cleaner fix ./content --no-voice
watermark-cleaner fix ./assets --aggressive     # also replace homoglyphs and strip variation selectors
watermark-cleaner check . --json

check is safe to run over an entire folder to see what is inside without touching anything. Exit codes: check returns 1 when a file contains AI phrases or AI sentence shapes, so it works as a publish gate. fix returns 0 after cleaning what it can; pass --strict to make fix return 1 when blocking findings remain that need a human rewrite.

Markdown structure is protected: typography and voice rules never touch fenced code blocks, inline code or YAML frontmatter, so code examples in documentation survive cleaning. Invisible characters are still removed inside code, because hidden characters in code are exactly the Trojan Source attack this tool defends against. Disable with "protect_code": false if you want full-document typography.

What it removes, and what it deliberately keeps

Deliberately preserved, because removing them breaks legitimate text:

• Zero width joiner (U+200D) in emoji sequences.
• Zero width non-joiner (U+200C) when adjacent to a script that requires it (Persian, Arabic, Indic scripts and others). Between latin letters it is a watermark and gets removed.
• Bidi marks and bidi controls in documents that contain right-to-left text. In pure left-to-right documents they are removed, which is the Trojan Source defense.
• Non breaking spaces between digits (12 000 keeps its formatting) unless you disable keep_nbsp_in_numbers.
• Variation selectors, unless you pass --aggressive.

Image metadata, losslessly

watermark-cleaner fix strips EXIF, XMP, C2PA and comment segments from JPEG, PNG and WebP at the container level. Pixels are never re-encoded, so there is no quality loss and the operation is verifiable with a byte diff. ICC color profiles are kept by default, because removing them visibly shifts colors in the browser; strip them too with "strip_icc": true or --aggressive. SVG metadata, XMP blocks and XML comments are removed as text. GIF and TIFF are never modified; the tool tells you it cannot strip them losslessly and leaves them alone. Files that fail to parse are left untouched.

Use as a publish gate

With the pre-commit framework:

repos:
  - repo: https://github.com/pixelstrunk/watermark-cleaner
    rev: v0.2.0
    hooks:
      - id: watermark-cleaner-fix

watermark-cleaner-fix cleans the mechanical layers on every commit and never blocks on style. Add id: watermark-cleaner-check if you also want commits blocked on AI phrases. A plain git hook and a deploy gate script live in integrations/.

Coding agents can drive the CLI through the skill in skills/watermark-cleaner/, which enforces an inspect-first workflow.

All writes are safe by construction: files are replaced atomically, writes through symlinks are refused, and files larger than max_file_bytes (default 256 MiB) are skipped instead of loaded into memory.

The voice layer, honestly

Cleaning AI text falls into buckets, and this tool is precise about which bucket each action lives in.

The voice layer auto-deletes only phrases that are pure filler ("without further ado"). When a deletion opens a sentence, the sentence is repaired: leftover spaces go away and the next word is capitalized. Phrases that carry an object ("let's explore the API") are never cut mid-sentence; they are flagged as errors for a human to rewrite. The phrase rulebook is currently English only.

Non-goals, stated plainly

• It does not remove statistical text watermarks (the SynthID style signal that some vendors embed in word choice). No deterministic tool can, and there is no public detector to verify removal. Only substantial rewriting degrades that signal.
• It does not auto-rewrite AI sentence shapes. Rewriting a sentence needs judgment, so the tool detects and blocks those shapes instead of replacing them and producing nonsense.
• It does not certify that text will pass any AI detector, and it is not a way to misrepresent authorship.

Configure

Drop a watermark-cleaner.config.json in a project root. Any key overrides the default.

{
  "voice": true,
  "replace_homoglyphs": false,
  "keep_nbsp_in_numbers": true,
  "protect_code": true,
  "strip_icc": false,
  "dash_policy": { "spaced_replacement": " - ", "unspaced_replacement": "-" },
  "custom_banned_phrases": ["our innovative product", "leverage synergies"],
  "ignore_phrases": ["delve", "not-only-but-also"],
  "text_extensions": [".md", ".mdx", ".txt"],
  "exclude": ["node_modules", ".git", "dist"]
}

By default a spaced em dash ("fast — slow") becomes a comma ("fast, slow") and an unspaced one becomes a hyphen. That is an opinionated policy; dash_policy lets you change both replacements per project.

custom_banned_phrases adds your own blocking phrases on top of the rulebook. ignore_phrases switches off any built-in phrase, lexicon word or sentence shape id that does not fit your domain.

Optional DeepL rewrite

export DEEPL_API_KEY=your-key
watermark-cleaner rewrite post.md
watermark-cleaner rewrite post.md --source-lang DE --pivot-lang EN
watermark-cleaner rewrite post.md --write

The document language is auto-detected by DeepL when --source-lang is not given.

This back translates the text (source to pivot and back) using a model that is not the one that wrote the text, then runs the deterministic cleaner on the result. It changes word choice, which is what degrades a statistical watermark, but it can shift meaning and it sends your text to DeepL. It is off by default and opt in. Do not use it on confidential content. It cannot certify that any vendor detector will fail.

Rules are one source

All character lists and phrase lists live in rules/*.json. The Python and Node packages read the same files, scripts/sync-rules.sh copies them into each package, and CI fails when they drift or when the two CLIs produce different output. Edit the rules once, both tools follow. Contributions to the rulebook are the easiest way to help; see CONTRIBUTING.md.

Scope and intent

This tool is for hygiene, privacy and transparency on content you own: see what is hidden in your text, and publish without machine fingerprints you did not choose to include.

License

MIT