  The commercialization of text-to-image diffusion models (DMs) brings forth
potential copyright concerns. Despite numerous attempts to protect DMs from
copyright issues, the vulnerabilities of these solutions are underexplored. In
this study, we formalized the Copyright Infringement Attack on generative AI
models and proposed a backdoor attack method, SilentBadDiffusion, to induce
copyright infringement without requiring access to or control over training
processes. Our method strategically embeds connections between pieces of
copyrighted information and text references in poisoning data while carefully
dispersing that information, making the poisoning data inconspicuous when
integrated into a clean dataset. Our experiments show the stealth and efficacy
of the poisoning data. When given specific text prompts, DMs trained with a
poisoning ratio of 0.20% can produce copyrighted images. Additionally, the
results reveal that the more sophisticated the DMs are, the easier the success
of the attack becomes. These findings underline potential pitfalls in the
prevailing copyright protection strategies and underscore the necessity for
increased scrutiny to prevent the misuse of DMs.
