ATVis: Understanding and diagnosing adversarial training processes through visual analytics
Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions. Despite enhancing robustness, adversarial training often results in a trade-off with standa...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Visual Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468502X24000639 |
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| _version_ | 1850059006290165760 |
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| author | Fang Zhu Xufei Zhu Xumeng Wang Yuxin Ma Jieqiong Zhao |
| author_facet | Fang Zhu Xufei Zhu Xumeng Wang Yuxin Ma Jieqiong Zhao |
| author_sort | Fang Zhu |
| collection | DOAJ |
| description | Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions. Despite enhancing robustness, adversarial training often results in a trade-off with standard accuracy on normal data, a phenomenon that remains a contentious issue. In addition, the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve. This paper introduces ATVis, a visual analytics framework for examining and diagnosing adversarial training processes. Through multi-level visualization design, ATVis enables the examination of model robustness from various granularity, facilitating a detailed understanding of the dynamics in the training epochs. The framework reveals the complex relationship between adversarial robustness and standard accuracy, which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training. The effectiveness of the framework is demonstrated through case studies. |
| format | Article |
| id | doaj-art-a0404862fe2141f4ba7cbda8f87a685b |
| institution | DOAJ |
| issn | 2468-502X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Visual Informatics |
| spelling | doaj-art-a0404862fe2141f4ba7cbda8f87a685b2025-08-20T02:51:00ZengElsevierVisual Informatics2468-502X2024-12-0184718410.1016/j.visinf.2024.10.003ATVis: Understanding and diagnosing adversarial training processes through visual analyticsFang Zhu0Xufei Zhu1Xumeng Wang2Yuxin Ma3Jieqiong Zhao4Southern University of Science and Technology, Shenzhen, ChinaSouthern University of Science and Technology, Shenzhen, ChinaDISSec, Nankai University, Tianjin, China; Corresponding authors.Southern University of Science and Technology, Shenzhen, China; Corresponding authors.Augusta University, Augusta, Georgia, USAAdversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions. Despite enhancing robustness, adversarial training often results in a trade-off with standard accuracy on normal data, a phenomenon that remains a contentious issue. In addition, the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve. This paper introduces ATVis, a visual analytics framework for examining and diagnosing adversarial training processes. Through multi-level visualization design, ATVis enables the examination of model robustness from various granularity, facilitating a detailed understanding of the dynamics in the training epochs. The framework reveals the complex relationship between adversarial robustness and standard accuracy, which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training. The effectiveness of the framework is demonstrated through case studies.http://www.sciencedirect.com/science/article/pii/S2468502X24000639Visual analyticsExplainable AIAdversarial training |
| spellingShingle | Fang Zhu Xufei Zhu Xumeng Wang Yuxin Ma Jieqiong Zhao ATVis: Understanding and diagnosing adversarial training processes through visual analytics Visual Informatics Visual analytics Explainable AI Adversarial training |
| title | ATVis: Understanding and diagnosing adversarial training processes through visual analytics |
| title_full | ATVis: Understanding and diagnosing adversarial training processes through visual analytics |
| title_fullStr | ATVis: Understanding and diagnosing adversarial training processes through visual analytics |
| title_full_unstemmed | ATVis: Understanding and diagnosing adversarial training processes through visual analytics |
| title_short | ATVis: Understanding and diagnosing adversarial training processes through visual analytics |
| title_sort | atvis understanding and diagnosing adversarial training processes through visual analytics |
| topic | Visual analytics Explainable AI Adversarial training |
| url | http://www.sciencedirect.com/science/article/pii/S2468502X24000639 |
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