Numerous deep learning algorithms have been inspired by and understood via the notion of
information bottleneck, where unnecessary information is (often implicitly) minimized while …

Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …

Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Abstract Deep Neural Networks (DNNs) have been demonstrated to perform exceptionally
well on most recognition tasks such as image classification and segmentation. However …

Obtaining deep networks that are robust against adversarial examples and generalize well
is an open problem. A recent hypothesis even states that both robust and accurate models …

Current state-of-the-art object recognition models are largely based on convolutional neural
network (CNN) architectures, which are loosely inspired by the primate visual system …

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

In recent years, with rapid technological advancement in both computing hardware and
algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human …

Although deep neural networks (DNNs) have achieved tremendous performance in diverse
vision challenges, they are surprisingly susceptible to adversarial examples, which are born …

Deep neural networks (DNNs) are vulnerable to adversarial noises, which motivates the
benchmark of model robustness. Existing benchmarks mainly focus on evaluating defenses …