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Filigrane de texte en Python : détecter l'écriture copiée

Towards Data Science •
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On August 2, 2026, Anthropic began watermarking every piece of text Claude produces. Since 2024, Gemini (Google) has used Synth ID-Text, a method they published in Nature and later open-sourced. Open AI built something similar.

China has required embedded labels on AI-generated content since September 2025, and nearly 190 organizations have signed the EU’s transparency code. The result: billions of words generated from these systems every day carrying an invisible mark—not metadata that vanishes on copy-paste, but a signal baked into the words themselves. So if AI companies can do that, can you? Most people assume no.

Images have pixels to tweak; audio has a spectrum. Text is just characters. If someone scrapes your writing and claims it, it’s your word against theirs.

That assumption is wrong. A century ago, mapmakers, dictionary editors, and at least one very annoyed lyrics company figured this out long before language models existed. You can watermark plain text three ways, each leaving a detectable signal: Invisible characters: easiest to add, easiest to erase.

Any sanitizer or chatbot pass wipes them out. Keyed word choices: specific substitutions based on a hidden key. Survives light editing, but a full rewrite kills it.

Meaning-level marks (rigged sampling): hardest to remove. It holds up better under rewriting, but the signal weakens. Durability always costs strength.

A text watermark isn’t a visible stamp. It’s a pattern of choices only you know.