Meta Gradient Adversarial Attack

• URL: http://arxiv.org/abs/2108.04204v2
• Date: Tue, 10 Aug 2021 06:22:51 GMT
• Title: Meta Gradient Adversarial Attack
• Authors: Zheng Yuan, Jie Zhang, Yunpei Jia, Chuanqi Tan, Tao Xue, Shiguang Shan
• Abstract summary: This paper proposes a novel architecture called Metaversa Gradient Adrial Attack (MGAA), which is plug-and-play and can be integrated with any existing gradient-based attack method. Specifically, we randomly sample multiple models from a model zoo to compose different tasks and iteratively simulate a white-box attack and a black-box attack in each task. By narrowing the gap between the gradient directions in white-box and black-box attacks, the transferability of adversarial examples on the black-box setting can be improved.
• Score: 64.5070788261061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper proposes a novel architecture called Meta Gradient Adversarial Attack (MGAA), which is plug-and-play and can be integrated with any existing gradient-based attack method for improving the cross-model transferability. Specifically, we randomly sample multiple models from a model zoo to compose different tasks and iteratively simulate a white-box attack and a black-box attack in each task. By narrowing the gap between the gradient directions in white-box and black-box attacks, the transferability of adversarial examples on the black-box setting can be improved. Extensive experiments on the CIFAR10 and ImageNet datasets show that our architecture outperforms the state-of-the-art methods for both black-box and white-box attack settings.

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