Adversarial Training: A Survey

• URL: http://arxiv.org/abs/2410.15042v1
• Date: Sat, 19 Oct 2024 08:57:35 GMT
• Title: Adversarial Training: A Survey
• Authors: Mengnan Zhao, Lihe Zhang, Jingwen Ye, Huchuan Lu, Baocai Yin, Xinchao Wang,
• Abstract summary: Adversarial training (AT) refers to integrating adversarial examples into the training process. Recent studies have demonstrated the effectiveness of AT in improving the robustness of deep neural networks against diverse adversarial attacks.
• Score: 130.89534734092388
• License:
• 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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