Core Concepts
The proposed Virtually Assured Amplification Attack (VA3) framework significantly amplifies the probability of generating copyright-infringing content on text-to-image generative models with probabilistic copyright protection mechanisms.
Abstract
The paper introduces the Virtually Assured Amplification Attack (VA3), a novel online attack framework that exposes the vulnerabilities of probabilistic copyright protection methods for text-to-image generative models.
Key highlights:
The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content.
Probabilistic copyright protection methods, such as Near Access-Freeness (NAF), provide a probabilistic guarantee against such infringement.
The proposed VA3 framework significantly amplifies the probability of generating infringing content through persistent interactions with the generative model and a non-trivial lower-bound on the success probability of each engagement.
The theoretical and experimental results demonstrate the effectiveness of the VA3 approach under various scenarios, highlighting the potential risk of implementing probabilistic copyright protection in practical applications of text-to-image generative models.
The paper also introduces Anti-NAF, a theoretically motivated adversarial prompt optimization algorithm tailored for NAF copyright protection, to generate prompts that fulfill the conditions for the amplification attack.
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