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 …

Recent research finds CNN models for image classification demonstrate overlapped
adversarial vulnerabilities: adversarial attacks can mislead CNN models with small …

The evolution of automotive technology will eventually permit the automated driving system
on the vehicle to handle all circumstances. Human occupants will be just passengers. This …

Deep Neural Networks are vulnerable to adversarial attacks even in settings where the
attacker has no direct access to the model being attacked. Such attacks usually rely on the …

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 …

This survey presents an overview of verification techniques for autonomous systems, with a
focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents …

This survey paper provides an overview of the current state of AI attacks and risks for AI
security and privacy as artificial intelligence becomes more prevalent in various applications …

While deep neural networks have been achieving state-of-the-art performance across a
wide variety of applications, their vulnerability to adversarial attacks limits their widespread …

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and
these malicious samples often transfer, ie, they remain adversarial even against other …

This paper presents a simple and effective visual prompting method for adapting pre-trained
models to downstream recognition tasks. Our method includes two key designs. First, rather …