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arXiv · 2107.01809

Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks

Xiao Yang·Yinpeng Dong·Tianyu Pang·Hang Su·Jun Zhu
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Abstract

Transfer-based adversarial attacks can evaluate model robustness in the black-box setting. Several methods have demonstrated impressive untargeted transferability, however, it is still challenging to efficiently produce targeted transferability. To this end, we develop a simple yet effective framework to craft targeted transfer-based adversarial examples, applying a hierarchical generative network. In particular, we contribute to amortized designs that well adapt to multi-class targeted attacks. Extensive experiments on ImageNet show that our method improves the success rates of targeted black-box attacks by a significant margin over the existing methods -- it reaches an average success rate of 29.1\% against six diverse models based only on one substitute white-box model, which significantly outperforms the state-of-the-art gradient-based attack methods. Moreover, the proposed method is also more efficient beyond an order of magnitude than gradient-based methods.

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Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu. 2021-07-05. Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks. https://arxiv.org/abs/2107.01809

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