Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is
necessary to evaluate their robustness against adversarial examples in the real world …

Physical adversarial attacks against deep neural networks (DNNs) have recently gained
increasing attention. The current mainstream physical attacks use printed adversarial …

Recently, physical adversarial attacks have been presented to evade DNNs-based object
detectors. To ensure the security, many scenarios are simultaneously deployed with visible …

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics,
economics, psychology, sports, etc. Given the enormous social impact and the consequent …

Patch-based physical attacks have increasingly aroused concerns. However, most existing
methods focus on obscuring targets captured on the ground, and some of these methods are …

Fast adversarial training (FAT) is an efficient method to improve robustness in white-box
attack scenarios. However, the original FAT suffers from catastrophic overfitting, which …

Deep neural networks (DNNs) are vulnerable to adversarial examples generated by adding
subtle perturbations to benign inputs. While these perturbations are somewhat small due to …

Deep Neural Networks (DNNs) have shown impressive performance in computer vision
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …