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 …

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

Most prior works on physical adversarial attacks mainly focus on the attack performance but
seldom enforce any restrictions over the appearance of the generated adversarial patches …

Nowadays, cameras equipped with AI systems can capture and analyze images to detect
people automatically. However, the AI system can make mistakes when receiving …

Deep learning models are vulnerable to adversarial examples. As a more threatening type
for practical deep learning systems, physical adversarial examples have received extensive …

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 …

In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR)
plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep …

Though deep neural networks (DNNs) have demonstrated excellent performance in
computer vision, they are susceptible and vulnerable to carefully crafted adversarial …

We present a systematic study of the transferability of adversarial attacks on state-of-the-art
object detection frameworks. Using standard detection datasets, we train patterns that …

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 …