As self-driving systems become better, simulating scenarios where the autonomy stack may
fail becomes more important. Traditionally, those scenarios are generated for a few scenes …

This article introduces the concept of Artificial Intelligence (AI) to a criminological audience.
After a general review of the phenomenon (including brief explanations of important cognate …

Thermal infrared detection systems play an important role in many areas such as night
security, autonomous driving, and body temperature detection. They have the unique …

Many works have demonstrated that neural networks are vulnerable to adversarial
examples. We examine the adversarial sticker attack, where the attacker places a sticker …

Object detection based on pre-trained deep neural networks (DNNs) has achieved
impressive performance and enabled many applications. However, DNN-based object …

It has been well recognized that neural network based image classifiers are easily fooled by
images with tiny perturbations crafted by an adversary. There has been a vast volume of …

Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to
ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate …

The continuous advances in the technology of Convolutional Neural Network (CNN) and
Deep Learning have been applied to facilitate various tasks of human life. However, security …

Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where
adversarial perturbations to the inputs can change or manipulate classification. To defend …

This paper presents a novel patch-based adversarial attack pipeline that trains adversarial
patches on 3D human meshes. We sample triangular faces on a reference human mesh …