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The commercialization of diffusion models, renowned for their ability togenerate high-quality images that are often indistinguishable from real ones,brings forth potential copyright concerns. Although attempts have been made toimpede unauthorized access to copyrighted material during training and tosubsequently prevent DMs from generating copyrighted images, the effectivenessof these solutions remains unverified. This study explores the vulnerabilitiesassociated with copyright protection in DMs by introducing a backdoor datapoisoning attack (SilentBadDiffusion) against text-to-image diffusion models.Our attack method operates without requiring access to or control over thediffusion model's training or fine-tuning processes; it merely involves theinsertion of poisoning data into the clean training dataset. This data,comprising poisoning images equipped with prompts, is generated by leveragingthe powerful capabilities of multimodal large language models and text-guidedimage inpainting techniques. Our experimental results and analysis confirm themethod's effectiveness. By integrating a minor portion ofnon-copyright-infringing stealthy poisoning data into the cleandataset-rendering it free from suspicion-we can prompt the finetuned diffusionmodels to produce copyrighted content when activated by specific triggerprompts. These findings underline potential pitfalls in the prevailingcopyright protection strategies and underscore the necessity for increasedscrutiny and preventative measures against the misuse of DMs.

The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline