Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think
This challenges the assumption that diffusion models are vulnerable to adversarial attacks, highlighting a critical oversight in current image protection methods.
The paper demonstrates that diffusion models in pixel space (PDMs) are highly robust to adversarial attacks, unlike latent diffusion models (LDMs), and shows that PDMs can effectively purify adversarial patterns from LDMs.
Adversarial examples for diffusion models are widely used as solutions for safety concerns. By adding adversarial perturbations to personal images, attackers can not edit or imitate them easily. However, it is essential to note that all these protections target the latent diffusion model (LDMs), the adversarial examples for diffusion models in the pixel space (PDMs) are largely overlooked. This may mislead us to think that the diffusion models are vulnerable to adversarial attacks like most deep models. In this paper, we show novel findings that: even though gradient-based white-box attacks can be used to attack the LDMs, they fail to attack PDMs. This finding is supported by extensive experiments of almost a wide range of attacking methods on various PDMs and LDMs with different model structures, which means diffusion models are indeed much more robust against adversarial attacks. We also find that PDMs can be used as an off-the-shelf purifier to effectively remove the adversarial patterns that were generated on LDMs to protect the images, which means that most protection methods nowadays, to some extent, cannot protect our images from malicious attacks. We hope that our insights will inspire the community to rethink the adversarial samples for diffusion models as protection methods and move forward to more effective protection. Codes are available in https://github.com/xavihart/PDM-Pure.