Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

EXplainable artificial intelligence (XAI) is an emerging research area relating to the creation
of machine learning algorithms from which explanations for outputs are provided. In many …

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 …

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 …

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 …

Existing DeepFake detection methods focus on passive detection, ie, they detect fake face
images by exploiting the artifacts produced during DeepFake manipulation. These detection …