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arXiv · 2410.15042

Adversarial Training: A Survey

Mengnan Zhao·Lihe Zhang·Jingwen Ye·Huchuan Lu·Baocai Yin·Xinchao Wang
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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.

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Mengnan Zhao, Lihe Zhang, Jingwen Ye, Huchuan Lu, Baocai Yin, Xinchao Wang. 2024-10-19. Adversarial Training: A Survey. https://arxiv.org/abs/2410.15042

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