Based-CE white-box adversarial attack will not work using super-fitting

• URL: http://arxiv.org/abs/2205.02741v1
• Date: Wed, 4 May 2022 09:23:00 GMT
• Title: Based-CE white-box adversarial attack will not work using super-fitting
• Authors: Youhuan Yang, Lei Sun, Leyu Dai, Song Guo, Xiuqing Mao, Xiaoqin Wang and Bayi Xu
• Abstract summary: Deep Neural Networks (DNN) are widely used in various fields due to their powerful performance. Recent studies have shown that deep learning models are vulnerable to adversarial attacks. This paper proposes a new defense method by using the model super-fitting status.
• Score: 10.34121642283309
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks (DNN) are widely used in various fields due to their powerful performance, but recent studies have shown that deep learning models are vulnerable to adversarial attacks-by adding a slight perturbation to the input, the model will get wrong results. It is especially dangerous for some systems with high security requirements, so this paper proposes a new defense method by using the model super-fitting status. Model's adversarial robustness (i.e., the accuracry under adversarial attack) has been greatly improved in this status. This paper mathematically proves the effectiveness of super-fitting, and proposes a method to make the model reach this status quickly-minimaze unrelated categories scores (MUCS). Theoretically, super-fitting can resist any existing (even future) Based on CE white-box adversarial attack. In addition, this paper uses a variety of powerful attack algorithms to evaluate the adversarial robustness of super-fitting and other nearly 50 defense models from recent conferences. The experimental results show that super-fitting method in this paper can make the trained model obtain the highest adversarial performance robustness.

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