Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.15042 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909356478431232 |
|---|---|
| author | Zhao, Mengnan Zhang, Lihe Ye, Jingwen Lu, Huchuan Yin, Baocai Wang, Xinchao |
| author_facet | Zhao, Mengnan Zhang, Lihe Ye, Jingwen Lu, Huchuan Yin, Baocai Wang, Xinchao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adversarial Training: A Survey Zhao, Mengnan Zhang, Lihe Ye, Jingwen Lu, Huchuan Yin, Baocai Wang, Xinchao Machine Learning Artificial Intelligence 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. |
| title | Adversarial Training: A Survey |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.15042 |