Summary of Adbm: Adversarial Diffusion Bridge Model For Reliable Adversarial Purification, by Xiao Li et al.
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
by Xiao Li, Wenxuan Sun, Huanran Chen, Qiongxiu Li, Yining Liu, Yingzhe He, Jie Shi, Xiaolin Hu
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 A recently proposed diffusion-based defense method called DiffPure has been shown to be effective against adversarial examples. However, we found that DiffPure’s performance is suboptimal due to an inherent trade-off between noise purification and data recovery quality. Moreover, existing evaluations of DiffPure rely on weak adaptive attacks, which raises concerns about their reliability. To address these limitations, we propose the Adversarial Diffusion Bridge Model (ADBM), which constructs a reverse bridge from the diffused adversarial data back to its original clean examples. Through theoretical analysis and experimental validation across various scenarios, ADBM has been shown to be a superior and robust defense mechanism. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to protect computer systems from bad data. This method uses something called diffusion models. These models can help remove the bad data and make things cleaner again. But there are some problems with how this works right now. The system isn’t perfect, and it can only do so much. To fix these issues, researchers have created a new model that helps bridge the gap between clean and dirty data. This new model is called ADBM, short for Adversarial Diffusion Bridge Model. It’s being tested to see if it works better than the old way. |
Keywords
* Artificial intelligence * Diffusion
