Summary of Lorid: Low-rank Iterative Diffusion For Adversarial Purification, by Geigh Zollicoffer et al.
LoRID: Low-Rank Iterative Diffusion for Adversarial Purification
by Geigh Zollicoffer, Minh Vu, Ben Nebgen, Juan Castorena, Boian Alexandrov, Manish Bhattarai
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 The paper presents a theoretical analysis of diffusion-based purification methods for removing malicious perturbations from adversarial examples. It introduces LoRID, a novel method that leverages multi-stage diffusion-denoising loops and Tucker decomposition to remove adversarial noise. The authors demonstrate the effectiveness of LoRID in CIFAR-10/100, CelebA-HQ, and ImageNet datasets under both white-box and black-box settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to purify images from unwanted noise using diffusion-based methods. It shows that by using a new approach called LoRID, we can remove this noise more effectively than before. This is important for protecting computer systems and networks from attacks. |
Keywords
» Artificial intelligence » Diffusion
