Mitigating Watermark Forgery in Generative Models via Randomized Key Selection

Abstract:Watermarking enables GenAI providers to verify whether content was generated by their models. A watermark is a hidden signal in the content, whose presence can be detected using a secret watermark key. A core security threat are forgery attacks, where adversaries insert the provider's watermark into content \emph{not} produced by the provider, potentially damaging their reputation and undermining trust. Existing defenses resist forgery by embedding many watermarks with multiple keys into the same content, which can degrade model utility. However, forgery remains a threat when attackers can collect sufficiently many watermarked samples. We propose a defense with a sample-count-independent upper bound on forgery success for blind attackers, conditional on key-symmetric, independent detector outcomes. Our scheme does not further degrade model utility. We randomize the watermark key selection for each query and accept content as genuine only if a watermark is detected by \emph{exactly} one key. Unlike cryptographic watermarks that rely on computational hardness assumptions and require designing new watermarking schemes from scratch, our method can be applied to any existing watermarking method to improve its forgery resistance. We focus on text watermarking, but our defense is modality-agnostic, since it treats the underlying watermarking method as a black-box. To show this, we include a preliminary study on image watermarking using Tree-Ring. Separately from this conditional guarantee, we empirically observe that, at $r=4$ keys, harmful-text forgery success drops from as high as $87\%$ with a single key to as low as $1\%$ against the adaptive blind attackers that we evaluate, at negligible computational overhead; a preliminary image study shows a reduction from $100\%$ to $2\%$.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2507.07871 [cs.CR]

(or arXiv:2507.07871v5 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2507.07871 Focus to learn more arXiv-issued DOI via DataCite

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