Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Moreover, the transferability of the adversarial examples has received broad attention in recent years, which means that adversarial examples crafted by a surrogate model can also attack unknown models. This phenomenon gave birth to the transfer-based adversarial attacks, which aim to improve the transferability of the generated adversarial examples. In this paper, we propose to improve the transferability of adversarial examples in the transfer-based attack via masking unimportant parameters (MUP). The key idea in MUP is to refine the pretrained surrogate models to boost the transfer-based attack. Based on this idea, a Taylor expansion-based metric is used to evaluate the parameter importance score and the unimportant parameters are masked during the generation of adversarial examples. This process is simple, yet can be naturally combined with various existing gradient-based optimizers for generating adversarial examples, thus further improving the transferability of the generated adversarial examples. Extensive experiments are conducted to validate the effectiveness of the proposed MUP-based methods.

翻译：深度神经网络（DNNs）已被证明易受对抗样本攻击。近年来，对抗样本的迁移性受到广泛关注，即由替代模型生成的对抗样本同样能攻击未知模型。这一现象催生了基于迁移的对抗攻击方法，其核心目标在于提升生成对抗样本的迁移性。本文提出通过遮蔽不重要参数（MUP）来增强基于迁移攻击中对抗样本的迁移性能。MUP的核心思想是对预训练替代模型进行精炼以提升迁移攻击效果。基于此思想，采用泰勒展开度量评估参数重要性得分，在对抗样本生成过程中遮蔽不重要的参数。该方法流程简洁，可自然与现有多种基于梯度的对抗样本生成优化器结合，从而进一步提升生成对抗样本的迁移性。通过大量实验验证了所提MUP方法体系的有效性。