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 …

Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to
adversarial examples. We find, however, that typical adaptive evaluations are incomplete …

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 …

Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe
driving behaviors. While many prior works aim to achieve higher prediction accuracy, few …

Certified patch defenses can guarantee robustness of an image classifier to arbitrary
changes within a bounded contiguous region. But, currently, this robustness comes at a cost …

Abstract 3D point cloud data is increasingly used in safety-critical applications such as
autonomous driving. Thus, the robustness of 3D deep learning models against adversarial …

Adversarial examples are malicious inputs crafted to induce misclassification. Commonly
studied\emph {sensitivity-based} adversarial examples introduce semantically-small …

Recently, techniques have been developed to provably guarantee the robustness of a
classifier to adversarial perturbations of bounded L 1 and L 2 magnitudes by using …

Deep neural networks have been shown to be susceptible to adversarial examples–small,
imperceptible changes constructed to cause mis-classification in otherwise highly accurate …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …