  The booming use of text-to-image generative models has raised concerns about
their high risk of producing copyright-infringing content. While probabilistic
copyright protection methods provide a probabilistic guarantee against such
infringement, in this paper, we introduce Virtually Assured Amplification
Attack (VA3), a novel online attack framework that exposes the vulnerabilities
of these protection mechanisms. The proposed framework significantly amplifies
the probability of generating infringing content on the sustained interactions
with generative models and a non-trivial lower-bound on the success probability
of each engagement. Our theoretical and experimental results demonstrate the
effectiveness of our approach under various scenarios. These findings highlight
the potential risk of implementing probabilistic copyright protection in
practical applications of text-to-image generative models. Code is available at
https://github.com/South7X/VA3.
