6arXiv cs.LG (Machine Learning)·
Information-theoretic framework establishes tight sample complexity laws for watermark forensics in generative models
A new arXiv preprint develops an information-theoretic framework for watermark forensics in generative model outputs, organizing detection, attribution, payload extraction, and localization into a 'forensic ladder' with precise sample complexity bounds. The main theorem establishes the first tight entropy-rate law for multi-user attribution: attributing text to one of N users costs Θ(log N/h) tokens under statistically distortion-free schemes, with a matching converse. The paper also identifies two fundamental gaps — a window where text is provably machine-made but unattributable, and a footprint-resolution uncertainty principle — validated experimentally on GPT-2, Pythia-410M, and Qwen2.5.
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