  It has been demonstrated that deep neural networks are prone to noisy
examples particular adversarial samples during inference process. The gap
between robust deep learning systems in real world applications and vulnerable
neural networks is still large. Current adversarial training strategies improve
the robustness against adversarial samples. However, these methods lead to
accuracy reduction when the input examples are clean thus hinders the
practicability. In this paper, we investigate an approach that protects the
neural network classification from the adversarial samples and improves its
accuracy when the input examples are clean. We demonstrate the versatility and
effectiveness of our proposed approach on a variety of different networks and
datasets.
