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RDAT: an efficient regularized decoupled adversarial training mechanism

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Abstract

Adversarial examples have exposed the inherent vulnerabilities of deep neural networks. Although adversarial training has emerged as the leading strategy for adversarial defenses, it is frequently hindered by a challenging balance between maintaining accuracy on unaltered examples and enhancing model robustness. Recent efforts on decoupling network components can effectively reduce the degradation of classification accuracy, but at the cost of an unsatisfactory in robust accuracy, and may suffer from robust overfitting. In this paper, we delve into the underlying causes of this compromise, and introduce a novel framework, the Regularized Decoupled Adversarial Training Mechanism (RDAT) to effectively deal with the trade-off and overfitting. Specifically, RDAT comprises two distinct modules: Regularization module mitigates harmful perturbations by controlling the data distribution distance of examples before and after adversarial attacks. Decoupling training module separates clean and adversarial examples so that they can have special optimization strategies to avoid the suboptimal result in adversarial training. With marginal compromise on the classification accuracy, RDAT achieves remarkably better model robustness with the improvement of robust accuracy by an average of 4.47% on CIFAR-10 and 3.23% on CIFAR-100 when compared to state-of-the-art methods.

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Data availability

The CIFAR-10 [50], the CIFAR-100 [50], and the SVHN [51] datasets can be openly available.

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Acknowledgements

This work was supported by Dreams Foundation of Jianghuai Advance Technology Center (NO.2023-ZM01Z002) and the Natural Science Foundation of Hunan Province under Grant 2023JJ30082.

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Authors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Yishan Li, Yanming Guo, Yulun Wu, Yuxiang Xie, Mingrui Lao, Tianyuan Yu & Yirun Ruan

  2. Hunan Institute of Advanced Technology, Changsha, China

    Yanming Guo

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Contributions

Yishan Li wrote the main manuscript, and Yanming Guo and Yulun Wu gave guidance on the writing of the thesis and gave advice on revisions. Yuxiang Xie and Mingrui Lao produced the charts in the text. Tianyuan Yu and Yirun Ruan directed the experimental part. All authors have reviewed the manuscript

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Correspondence to Yanming Guo.

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Li, Y., Guo, Y., Wu, Y. et al. RDAT: an efficient regularized decoupled adversarial training mechanism. Int J Multimed Info Retr 13, 24 (2024). https://doi.org/10.1007/s13735-024-00330-y

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