Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability

TL;DR

This paper introduces a novel targeted attack method, ESMA, which improves transferability by perturbing easy samples in the target class, achieving higher success rates with less computational resources.

Contribution

The paper proposes ESMA, a generative targeted attack strategy that enhances transferability by focusing on easy samples, outperforming state-of-the-art methods in success rate and efficiency.

Findings

ESMA achieves higher targeted attack success rates.

ESMA requires only 5% of the storage space of SOTA methods.

ESMA attacks all classes with a single model, reducing computation time.

Abstract

The transferability of adversarial perturbations provides an effective shortcut for black-box attacks. Targeted perturbations have greater practicality but are more difficult to transfer between models. In this paper, we experimentally and theoretically demonstrated that neural networks trained on the same dataset have more consistent performance in High-Sample-Density-Regions (HSDR) of each class instead of low sample density regions. Therefore, in the target setting, adding perturbations towards HSDR of the target class is more effective in improving transferability. However, density estimation is challenging in high-dimensional scenarios. Further theoretical and experimental verification demonstrates that easy samples with low loss are more likely to be located in HSDR. Perturbations towards such easy samples in the target class can avoid density estimation for HSDR location. Based on the above facts, we verified that adding perturbations to easy samples in the target class improves targeted adversarial transferability of existing attack methods. A generative targeted attack strategy named Easy Sample Matching Attack (ESMA) is proposed, which has a higher success rate for targeted attacks and outperforms the SOTA generative method. Moreover, ESMA requires only 5% of the storage space and much less computation time comparing to the current SOTA, as ESMA attacks all classes with only one model instead of seperate models for each class. Our code is available at https://github.com/gjq100/ESMA.