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 https://github.com/South7X/VA3.

Researchers from Citadel developed a deep learning method to generate DNS amplification attacks. Deep learning algorithms have lately been shown to be quite effective at identifying and preventing cybersecurity assaults. Various deep learning techniques, such as those used for image classification and natural language processing, have been the target of numerous cybercriminals' recent development of new attacks. The most common of these strategies are adversarial attacks, which use altered data to deceive deep learning algorithms into categorizing it incorrectly. This could lead to the failure of numerous deep learning-based apps, biometric systems, and other systems.