Targeted Attack Improves Protection against Unauthorized Diffusion Customization

Diffusion models build a new milestone for image generation yet raising public concerns, for they can be fine-tuned on unauthorized images for customization. Protection based on adversarial attacks rises to encounter this unauthorized diffusion customization, by adding protective watermarks to images and poisoning diffusion models. However, current protection, leveraging untargeted attacks, does not appear to be effective enough. In this paper, we propose a simple yet effective improvement for the protection against unauthorized diffusion customization by introducing targeted attacks. We show that by carefully selecting the target, targeted attacks significantly outperform untargeted attacks in poisoning diffusion models and degrading the customization image quality. Extensive experiments validate the superiority of our method on two mainstream customization methods of diffusion models, compared to existing protections. To explain the surprising success of targeted attacks, we delve into the mechanism of attack-based protections and propose a hypothesis based on our observation, which enhances the comprehension of attack-based protections. To the best of our knowledge, we are the first to both reveal the vulnerability of diffusion models to targeted attacks and leverage targeted attacks to enhance protection against unauthorized diffusion customization. [Warning: This paper contains images that some readers may find disturbing.] * equal contribution Diffusion Models Diffusion Models (Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Ho et al., 2020) are built on a forward and reverse process that map the noise and data distribution. The forward process perturbs the data progressively to approximate Gaussian noise. The reverse process exploits a parameterized score-function s θ to generate data by denoising noise step-by-step. These