Summary of Pixel Is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think, by Haotian Xue and Yongxin Chen
Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think
by Haotian Xue, Yongxin Chen
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the robustness of diffusion models to adversarial attacks in both latent and pixel spaces. Current protections focus on latent diffusion models (LDMs), but neglect pixel space diffusion models (PDMs). The authors demonstrate that gradient-based white-box attacks can successfully target LDMs, whereas PDMs are more resilient. Extensive experiments were conducted using various attacking methods and model structures to support these findings. Additionally, the study shows that PDMs can be used as an off-the-shelf purifier to remove adversarial patterns generated on LDMs, rendering current protection methods ineffective. This research aims to inspire a reevaluation of adversarial samples for diffusion models as protection methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how safe are computer programs called diffusion models from being tricked by fake images. Currently, most protections focus on one type of model, but not the other. The study finds that some attacks can successfully fool the first type of model, but fail to fool the second. This is important because it means we need to rethink our protection methods to make sure our computer programs are truly secure. |
Keywords
» Artificial intelligence » Diffusion
