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<p>Fast adversarial training (FAT) has emerged as an effective strategy to enhance the robustness of deep learning models against adversarial attacks. However, catastrophic overfitting remains a significant challenge, where models experience a sudden deterioration in their adversarial robustness. To address this issue, we propose a novel approach combining momentum-driven initialization and max-norm regularization, termed MIMR. Our momentum-driven initialization method maintains high-quality adversarial examples throughout training by leveraging previous perturbations, without increasing computational costs. Furthermore, max-norm regularization restricts gradient updates, fostering a more stable and generalizable adversarial training framework. Theoretical evaluations support our proposed methods, which improve loss function smoothness. Experiments on benchmark datasets, including CIFAR-10, CIFAR-100, and Tiny ImageNet, demonstrate that MIMR not only mitigates catastrophic overfitting but also achieves higher classification accuracy under various attacks, such as PGD, C&amp;W, and AutoAttack. Specifically, MIMR performs exceptionally well under strong attacks, achieving 49.44% accuracy on CIFAR-10, 25.59% on CIFAR-100, and 17.26% on Tiny ImageNet for AutoAttack, showcasing superior robustness compared to existing methods. The source code is available at <a href="https://github.com/anjumiqbal06/MIMR">https://github.com/anjumiqbal06/MIMR</a>.</p>

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Enhancing fast adversarial training with momentum-driven initialization and max-norm regularization for robust deep learning models

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Anjum Iqbal,
  • Weiqiang Kong,
  • Yasir Iqbal,
  • Umer Sadiq Khan,
  • Shah Fahad Khan,
  • Ijaz Hussain

摘要

Fast adversarial training (FAT) has emerged as an effective strategy to enhance the robustness of deep learning models against adversarial attacks. However, catastrophic overfitting remains a significant challenge, where models experience a sudden deterioration in their adversarial robustness. To address this issue, we propose a novel approach combining momentum-driven initialization and max-norm regularization, termed MIMR. Our momentum-driven initialization method maintains high-quality adversarial examples throughout training by leveraging previous perturbations, without increasing computational costs. Furthermore, max-norm regularization restricts gradient updates, fostering a more stable and generalizable adversarial training framework. Theoretical evaluations support our proposed methods, which improve loss function smoothness. Experiments on benchmark datasets, including CIFAR-10, CIFAR-100, and Tiny ImageNet, demonstrate that MIMR not only mitigates catastrophic overfitting but also achieves higher classification accuracy under various attacks, such as PGD, C&W, and AutoAttack. Specifically, MIMR performs exceptionally well under strong attacks, achieving 49.44% accuracy on CIFAR-10, 25.59% on CIFAR-100, and 17.26% on Tiny ImageNet for AutoAttack, showcasing superior robustness compared to existing methods. The source code is available at https://github.com/anjumiqbal06/MIMR.