1. Adversarial Training: A Survey
- Author
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Zhao, Mengnan, Zhang, Lihe, Ye, Jingwen, Lu, Huchuan, Yin, Baocai, and Wang, Xinchao
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Adversarial training (AT) refers to integrating adversarial examples -- inputs altered with imperceptible perturbations that can significantly impact model predictions -- into the training process. Recent studies have demonstrated the effectiveness of AT in improving the robustness of deep neural networks against diverse adversarial attacks. However, a comprehensive overview of these developments is still missing. This survey addresses this gap by reviewing a broad range of recent and representative studies. Specifically, we first describe the implementation procedures and practical applications of AT, followed by a comprehensive review of AT techniques from three perspectives: data enhancement, network design, and training configurations. Lastly, we discuss common challenges in AT and propose several promising directions for future research.
- Published
- 2024