Adversarial Robustness

Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability

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.

Improving Adversarial Robustness Requires Revisiting Misclassified Examples

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 …

On the Convergence and Robustness of Adversarial Training

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 …