  Previous studies demonstrate DNNs' vulnerability to adversarial examples and
adversarial training can establish a defense to adversarial examples. In
addition, recent studies show that deep neural networks also exhibit
vulnerability to parameter corruptions. The vulnerability of model parameters
is of crucial value to the study of model robustness and generalization. In
this work, we introduce the concept of parameter corruption and propose to
leverage the loss change indicators for measuring the flatness of the loss
basin and the parameter robustness of neural network parameters. On such basis,
we analyze parameter corruptions and propose the multi-step adversarial
corruption algorithm. To enhance neural networks, we propose the adversarial
parameter defense algorithm that minimizes the average risk of multiple
adversarial parameter corruptions. Experimental results show that the proposed
algorithm can improve both the parameter robustness and accuracy of neural
networks.
