What is statistical language model watermark detection?
Definition
Statistical language model watermark detection replays the keyed rule used during generation and classifies observed tokens or tournament outcomes. It aggregates the evidence across a passage, then compares the result with a null model describing unwatermarked text.
The detector returns a score or probability rather than reading a visible mark. Its reliability depends on text length, entropy, key correctness, detector calibration, edits, language, model family, and the false-positive threshold chosen for the use case.
Acronyms and aliases
statistical LLM watermark detection acronymAI text watermark detector variant
Specialised terms
Related terms
Frequently asked questions
Why does watermark detection need many tokens?
Each token carries only weak evidence, so aggregation across enough independent or calibrated choices is needed to separate signal from chance.
Can statistical watermark detection produce false positives?
Yes. Any statistical test has an error rate, which should be calibrated and considered before making consequential attribution decisions.
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