On the Information-Theoretic Limits of Latent-Space Watermarking Through Pretrained Generators
Read the original on arXiv Machine Learning →The paper investigates latent‑space watermarking using pretrained generators, where a watermark encoder selects latent inputs based on a message and secret key to produce outputs with a specified conditional distribution. For finite alphabets, it derives inner and outer bounds on the rate–key trade‑off and characterizes the capacity region when the generator’s output uniquely determines the latent distribution. The study extends to jointly Gaussian models, identifies key sufficient statistics, optimally allocates secret‑key resources across modes, and analyzes robustness against regeneration attacks, providing compound capacity results and decay rates for repeated attacks.
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