  Deep neural networks are widely used in various fields because of their
powerful performance. However, recent studies have shown that deep learning
models are vulnerable to adversarial attacks, i.e., adding a slight
perturbation to the input will make the model obtain wrong results. This is
especially dangerous for some systems with high-security requirements, so this
paper proposes a new defense method by using the model super-fitting state to
improve the model's adversarial robustness (i.e., the accuracy under
adversarial attacks). This paper mathematically proves the effectiveness of
super-fitting and enables the model to reach this state quickly by minimizing
unrelated category scores (MUCS). Theoretically, super-fitting can resist any
existing (even future) CE-based white-box adversarial attacks. In addition,
this paper uses a variety of powerful attack algorithms to evaluate the
adversarial robustness of super-fitting, and the proposed method is compared
with nearly 50 defense models from recent conferences. The experimental results
show that the super-fitting method in this paper can make the trained model
obtain the highest adversarial robustness.
