Connected and autonomous vehicles (CAVs) will form the backbone of future next-
generation intelligent transportation systems (ITS) providing travel comfort, road safety …

Deep learning architectures are vulnerable to adversarial perturbations. They are added to
the input and alter drastically the output of deep networks. These instances are called …

Despite the tremendous success of deep neural networks in various learning problems, it
has been observed that adding intentionally designed adversarial perturbations to inputs of …

Recently, there has been an abundance of works on designing Deep Neural Networks
(DNNs) that are robust to adversarial examples. In particular, a central question is which …

Adversarial attacks have exposed the vulnerability of one-stage classifiers to carefully
crafted perturbations which were shown to drastically alter their predictions while remaining …

Minimally perturbed adversarial examples were shown to drastically reduce the
performance of one-stage classifiers while being imperceptible. This paper investigates the …

Existing work on adversarial attacks on classification tasks has focused on classifiers that
make use of simple hypothesis testing models. In this work, we study the vulnerability of …

Automated fault detection and diagnosis systems are critical to safe and efficient operation of
smart buildings. A significant amount of building data can be collected and analyzed to …

Adversarial attacks were shown to drastically degrade the performance of one-stage
classifiers while being undetectable. In this paper, we examine the susceptibility of both flat …

Despite the tremendous success of deep neural networks in various learning problems, it
has been observed that adding an intentionally designed adversarial perturbation to inputs …