AI watermark resistance checker
Text watermarking promises a way to mark AI-generated content so it can be detected later — but in practice the robustness of these schemes varies wildly, and a lot of teams over-trust them. This tool is an educational model of how the main text-watermarking approaches hold up against the edits a real user might apply: a light copy-edit, a full paraphrase, a round-trip translation, a summary, or simply retyping. It does not strip or detect any real watermark — it helps you reason about which approach is fit for purpose before you build on it.
How it works
You pick a watermarking approach — a statistical token-bias scheme that nudges the model toward a secret “green list” of tokens, a unicode or zero-width character insertion, an out-of-band metadata tag, or a fragile stylistic pattern. Then you pick an edit. The tool combines the two and produces an estimated survival likelihood with an explanation grounded in how each technique actually carries its signal. Token-bias watermarks survive light edits because most tokens are untouched, but collapse under paraphrasing that regenerates the wording. Unicode marks survive copy-paste but vanish on normalization or retyping. Metadata never survives a copy-paste of the visible text at all.
Tips and notes
- No text watermark is robust against full rewriting. If an adversary paraphrases the meaning in their own words, the signal is gone.
- Match the threat model to the scheme. Metadata is fine for cooperative provenance, useless against a motivated stripper.
- Combine signals. Robust provenance pairs a watermark with cryptographic content credentials and out-of-band logging, not a single technique.
- Treat detection as probabilistic. Build false-positive and false-negative tolerance into any policy that acts on a watermark verdict.