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 lower-bounded success probability of each
engagement. Our theoretical and experimental results demonstrate the
effectiveness of our approach and highlight the potential risk of implementing
probabilistic copyright protection in practical applications of text-to-image
generative models. Code is available at this https URL

通过对文本到图像生成模型进行持续交互和扩大每次交互的成功概率，我们引入了一种名为 VA3 的创新在线攻击框架，揭示了这些保护机制的漏洞，并详细论证了在文本到图像生成模型的实际应用中实施概率版权保护的潜在风险。