Watermark Forensics for Generative Models: An Information-Theoretic Perspective

Jul 15, 2026

A new arXiv preprint introduces an information-theoretic framework for watermark forensics in generative models, establishing fundamental limits on detection, attribution, and payload extraction. The authors derive tight entropy-rate laws, showing that attributing text to one of N users requires Θ(log N/h) tokens and extracting an ℓ-bit payload requires Θ(ℓ/h) tokens, where h is the entropy rate. Experiments on GPT-2, Pythia-410M, and Qwen2.5 confirm the theoretical predictions.

Why it matters: This work rigorously quantifies the trade-offs and costs of watermarking in generative AI, providing a theoretical foundation that can inform the design of more reliable forensic tools.

Full story at: arXiv Cryptography and Security ↗