Deep neural networks have been applied in many computer vision tasks and achieved state-
of-the-art performance. However, misclassification will occur when DNN predicts adversarial …

Deep neural networks (DNNs) have achieved state-of-the-art results in various pattern
recognition tasks. However, they perform poorly on out-of-distribution adversarial examples …

Convolutional Neural Networks (CNNs) are well-known for their vulnerability to adversarial
attacks, posing significant security concerns. In response to these threats, various defense …

Deep neural networks are susceptible to adversarial examples, posing a significant security
risk in critical applications. Adversarial Training (AT) is a well-established technique to …

It is broadly known that deep neural networks are susceptible to being fooled by adversarial
examples with perturbations imperceptible by humans. Various defenses have been …

Adversarial training integrates adversarial examples during model training to enhance
robustness. However, its application in fixed dataset settings differs from real-world …

This paper proposes a novel method, IB-RAR, which uses Information Bottleneck (IB) to
strengthen adversarial robustness for both adversarial training and non-adversarial-trained …

Modern deep neural networks have made numerous breakthroughs in real-world
applications, yet they remain vulnerable to some imperceptible adversarial perturbations …

Deep Neural Network (DNN) are vulnerable to adversarial attacks. As a countermeasure,
adversarial training aims to achieve robustness based on the min-max optimization problem …

Despite their high accuracy, deep neural networks (DNNs) are vulnerable to adversarial
examples. Currently, adversarial training is the mainstream defense approach against …