The study of adversarial examples and their activations have attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing works, in this paper, we highlight two new characteristics of …
We explore the relationship between the adversarial robustness and numerical stability. Furthermore, we propose IE-Skips, which is a modification of the vaniila skip connections for Residual Network Families inspired by the implicit Euler method and we also theoretically and exmperimently prove the advantages of our structure under adversarial attacks.
Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. A range of defense techniques have been proposed to improve DNN robustness to adversarial examples, among which adversarial training has been …
Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques, adversarial training with Projected Gradient Decent (PGD) is amongst …