Summary of Dat: Improving Adversarial Robustness Via Generative Amplitude Mix-up in Frequency Domain, by Fengpeng Li et al.
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain
by Fengpeng Li, Kemou Li, Haiwei Wu, Jinyu Tian, Jiantao Zhou
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes a novel approach to improve the robustness of deep neural networks (DNNs) against adversarial attacks. Adversarial training (AT) involves incorporating adversarial examples (AEs) into model training, but recent studies show that AEs disproportionately impact phase patterns in the frequency spectrum, leading to incorrect categorization. The proposed Dual Adversarial Training (DAT) strategy uses an optimized Adversarial Amplitude Generator (AAG) to mix amplitude features of training samples with distractor images, guiding the model to focus on phase patterns unaffected by adversarial perturbations. Experiments on various datasets demonstrate that DAT leads to significantly improved robustness against diverse attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer models more secure from fake data attacks. When someone tries to trick a computer model into making a mistake, the attack usually targets important patterns in the data. The proposed method, called Dual Adversarial Training, helps the model focus on these patterns and not be fooled by the fake data. This makes the model better at recognizing real data and rejecting fake data. By testing this approach on different datasets, the researchers found that it significantly improves the model’s ability to resist various types of attacks. |
