  Recently, the robustness of deep learning models has received widespread
attention, and various methods for improving model robustness have been
proposed, including adversarial training, model architecture modification,
design of loss functions, certified defenses, and so on. However, the principle
of the robustness to attacks is still not fully understood, also the related
research is still not sufficient. Here, we have identified a significant factor
that affects the robustness of models: the distribution characteristics of
softmax values for non-real label samples. We found that the results after an
attack are highly correlated with the distribution characteristics, and thus we
proposed a loss function to suppress the distribution diversity of softmax. A
large number of experiments have shown that our method can improve robustness
without significant time consumption.
