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Towards sustainable adversarial training with successive perturbation generation
Regular Papers | Updated：2024-04-29
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    • Towards sustainable adversarial training with successive perturbation generation

      Enhanced Publication
    • 基于连续扰动生成方法的可持续对抗训练
    • “在防御对抗性攻击和提升卷积神经网络模型鲁棒性方面，利用在线生成的对抗性例子进行对抗训练已展现出令人瞩目的性能。然而，现有方法大多专注于寻找强对抗性例子以迫使模型学习对抗数据分布，这不可避免地导致计算开销大且降低在干净数据上的泛化性能。本文提出了一种渐进式对抗性训练方案，即SPGAT，它通过连续增强对抗性例子的强度并跨训练周期迁移模型，有效提升了模型鲁棒性，同时保持了较低的计算成本。在MNIST、CIFAR-10和CIFAR-100等数据集上的实验结果表明，SPGAT在效率和性能上均优于现有方法，例如，其计算时间仅为标准对抗训练的22%，且在对抗准确率和干净准确率上分别提升了7%和3%。这一研究为提升神经网络模型鲁棒性提供了新的思路和方法。”
    • Frontiers of Information Technology & Electronic Engineering   Vol. 25, Issue 4, Pages: 527-539(2024)

    • DOI：10.1631/FITEE.2300474    

      CLC： TP391.1

    • Published：0 April 2024，

      Received：12 July 2023，

      Accepted：2023-10-08

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  • WEI LIN, LICHUAN LIAO. Towards sustainable adversarial training with successive perturbation generation. [J]. Frontiers of information technology & electronic engineering, 2024, 25(4): 527-539. DOI： 10.1631/FITEE.2300474.

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