Enhancing the Transferability of Adversarial Examples With Random Diversity Ensemble and Variance Reduction Augmentation
Sensen Zhang, Haibo Hong, Mande Xie · IEEE Transactions on Big Data · 2025
Currently, deep neural networks (DNNs) are susceptible to adversarial attacks, particularly when the network's structure and parameters are known, while most of the existing attacks do not perform satisfactorily in the presence of black-box settings. In this context, model augmentation is considered to be effective to improve the success rates of black-box attacks on adversarial examples. However, the existing model augmentation methods tend to rely on a single transformation, which limits the diversity of augmented model collections and thus affects the transferability of adversarial examples. In this paper, we first propose the random diversity ensemble method (RDE-MI-FGSM) to effectively enhance the diversity of the augmented model collection, thereby improving the transferability of the generated adversarial examples. Afterwards, we put forward the random diversity variance ensemble method (RDE-VRA-MI-FGSM), which adopts variance reduction augmentation (VRA) to improve the gradient variance of the enhanced model set and avoid falling into a poor local optimum, so as to further improve the transferability of adversarial examples. Furthermore, experimental results demonstrate that our approaches are compatible with many existing transfer-based attacks and can effectively improve the transferability of gradient-based adversarial attacks on the ImageNet dataset. Also, our proposals have achieved higher attack success rates even if the target model adopts advanced defenses. Specifically, we have achieved an average attack success rate of 91.4% on the defense model, which is higher than other baseline approaches.