Summary of Adversarial Training: a Survey, by Mengnan Zhao et al.
Adversarial Training: A Survey
by Mengnan Zhao, Lihe Zhang, Jingwen Ye, Huchuan Lu, Baocai Yin, Xinchao Wang
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A novel approach to enhance the robustness of deep neural networks is adversarial training (AT). By incorporating imperceptible perturbations into the training process, researchers have shown that AT can significantly improve model predictions against various types of attacks. Despite this progress, a comprehensive overview of these developments was lacking until now. This survey aims to fill this gap by reviewing recent and representative studies on AT. The paper describes the implementation procedures and practical applications of AT, followed by a thorough examination of AT techniques from three perspectives: data enhancement, network design, and training configurations. Additionally, common challenges in AT are discussed, along with proposed directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adversarial training is a way to make deep neural networks more reliable. It works by adding tiny changes to the training data that can trick the model into making mistakes. Researchers have found that this approach can help models withstand different types of attacks that try to fool them. This paper looks at many recent studies on AT and summarizes what they found out about how it works and why it’s important. |
