The transferability of adversarial examples is of central importance to transfer-based black-box adversarial attacks. Previous works for generating transferable adversarial examples focus on attacking \emph{given} pretrained surrogate models while the connections between surrogate models and adversarial trasferability have been overlooked. In this paper, we propose {\em Lipschitz Regularized Surrogate} (LRS) for transfer-based black-box attacks, a novel approach that transforms surrogate models towards favorable adversarial transferability. Using such transformed surrogate models, any existing transfer-based black-box attack can run without any change, yet achieving much better performance. Specifically, we impose Lipschitz regularization on the loss landscape of surrogate models to enable a smoother and more controlled optimization process for generating more transferable adversarial examples. In addition, this paper also sheds light on the connection between the inner properties of surrogate models and adversarial transferability, where three factors are identified: smaller local Lipschitz constant, smoother loss landscape, and stronger adversarial robustness. We evaluate our proposed LRS approach by attacking state-of-the-art standard deep neural networks and defense models. The results demonstrate significant improvement on the attack success rates and transferability. Our code is available at https://github.com/TrustAIoT/LRS.

提出了一种新颖的基于Lipschitz规正化的模拟模型（LRS）方法，用于转移性黑盒攻击中，通过改进模型参数以获得更好的攻击效果和转移性。研究还揭示了模拟模型的内在属性与转移性之间的关联，包括更小的局部Lipschitz常数、更平滑的损失曲面和更强的对抗鲁棒性。通过对现有深度神经网络和防御模型的攻击实验，结果表明使用LRS方法的攻击成功率和转移性显著提高。

LRS: 通过利普希茨正则化提升敌对迁移性