Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model
This work addresses the challenge of creating more effective adversarial attacks for security testing of deep neural networks, though it is incremental as it builds on existing gradient-based methods.
The paper tackles the problem of improving the transferability of adversarial examples in transfer-based attacks by masking unimportant parameters in surrogate models, resulting in enhanced attack success rates across various models, with experimental validation showing significant gains.
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.