通过双正则化损失与协同对抗生成，提升模型抗攻击能力。
D2R: dual regularization loss with collaborative adversarial generation for model robustness
- 设计双正则化损失，分步优化对抗与干净数据分布。
- 协同生成对抗样本，在广义模型间共享梯度信息。
- 在多个基准数据集上显著增强模型鲁棒性，适合防御研究者。
深度神经网络的鲁棒性对防御对抗攻击至关重要。现有防御方法虽采用协作学习框架提升鲁棒性，但仍存在两大缺陷：(i) 损失函数对目标模型指导不足；(ii) 对抗样本生成缺乏协作性。为此，我们提出双正则化损失（D2R Loss）和协同对抗生成（CAG）策略用于对抗训练。D2R Loss 包含两个优化步骤：通过对函数空间的合理探索，利用不同损失函数的优势，分别优化对抗分布与干净分布，从而更精准地聚焦目标模型的分布特性。CAG 通过引导模型与目标模型之间的基于梯度的协作生成对抗样本。我们在 CIFAR-10、CIFAR-100、Tiny ImageNet 三个基准数据集上，使用 WideResNet34-10 与 PreActResNet18 两种主流目标模型进行了大量实验。结果表明，结合 D2R Loss 与 CAG 可生成高度鲁棒的模型。
原文摘要 · Abstract (English)
The robustness of Deep Neural Network models is crucial for defending models against adversarial attacks. Recent defense methods have employed collaborative learning frameworks to enhance model robustness. Two key limitations of existing methods are (i) insufficient guidance of the target model via loss functions and (ii) non-collaborative adversarial generation. We, therefore, propose a dual regularization loss (D2R Loss) method and a collaborative adversarial generation (CAG) strategy for adversarial training. D2R loss includes two optimization steps. The adversarial distribution and clean distribution optimizations enhance the target model's robustness by leveraging the strengths of different loss functions obtained via a suitable function space exploration to focus more precisely on the target model's distribution. CAG generates adversarial samples using a gradient-based collaboration between guidance and target models. We conducted extensive experiments on three benchmark databases, including CIFAR-10, CIFAR-100, Tiny ImageNet, and two popular target models, WideResNet34-10 and PreActResNet18. Our results show that D2R loss with CAG produces highly robust models.
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