We review current and emerging knowledge-informed and brain-inspired cognitive systems
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …

Autonomous systems commonly rely on object detection and tracking (ODT) to perceive the
environment and predict the trajectory of surrounding objects for planning purposes. An …

When compared to the image classification models, black-box adversarial attacks against
video classification models have been largely understudied. This could be possible …

Adversarial attacks perturb images such that a deep neural network produces incorrect
classification results. A promising approach to defend against adversarial attacks on natural …

The success of deep neural networks (DNNs) has promoted the widespread applications of
person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to …

Blackbox transfer attacks for image classifiers have been extensively studied in recent years.
In contrast, little progress has been made on transfer attacks for object detectors. Object …

The majority of methods for crafting adversarial attacks have focused on scenes with a
single dominant object (eg, images from ImageNet). On the other hand, natural scenes …

Abstract Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial
examples, which are slightly perturbed input images which lead DNNs to make wrong …

Infrared remote sensing images are becoming increasingly popular due to their superior
penetration and resistance to light interference. However, challenges still remain when …

Recent studies show that deep person re-identification (re-ID) models are vulnerable to
adversarial examples, so it is critical to improving the robustness of re-ID models against …