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.
Abstract (translated)
URL
https://arxiv.org/abs/2205.02741
