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A2: Efficient Automated Attacker for Boosting Adversarial Training
Zhuoer Xu · Guanghui Zhu · Changhua Meng · shiwen cui · Zhenzhe Ying · Weiqiang Wang · Ming GU · Yihua Huang

Wed Dec 07 09:00 AM -- 11:00 AM (PST) @

Based on the significant improvement of model robustness by AT (Adversarial Training), various variants have been proposed to further boost the performance. Well-recognized methods have focused on different components of AT (e.g., designing loss functions and leveraging additional unlabeled data). It is generally accepted that stronger perturbations yield more robust models.However, how to generate stronger perturbations efficiently is still missed. In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training. A2 is a parameterized automated attacker to search in the attacker space for the best attacker against the defense model and examples. Extensive experiments across different datasets demonstrate that A2 generates stronger perturbations with low extra cost and reliably improves the robustness of various AT methods against different attacks.

Author Information

Zhuoer Xu (Nanjing University)
Guanghui Zhu (Nanjing University)
Changhua Meng (Ant Group)
shiwen cui (ant group)
Zhenzhe Ying
Weiqiang Wang (University of Southern California)
Ming GU (California Institute of Technology)
Yihua Huang (Nanjing University)

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