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The booming use of text-to-image generative models has raised concerns abouttheir high risk of producing copyright-infringing content. While probabilisticcopyright protection methods provide a probabilistic guarantee against suchinfringement, in this paper, we introduce Virtually Assured AmplificationAttack (VA3), a novel online attack framework that exposes the vulnerabilitiesof these protection mechanisms. The proposed framework significantly amplifiesthe probability of generating infringing content on the sustained interactionswith generative models and a non-trivial lower-bound on the success probabilityof each engagement. Our theoretical and experimental results demonstrate theeffectiveness of our approach under various scenarios. These findings highlightthe potential risk of implementing probabilistic copyright protection inpractical applications of text-to-image generative models. Code is available athttps://github.com/South7X/VA3.

VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models