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 …

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Estimating the risk level of adversarial examples is essential for safely deploying machine
learning models in the real world. One popular approach for physical-world attacks is to …

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 …

Human can easily recognize visual objects with lost information: even losing most details
with only contour reserved, eg cartoon. However, in terms of visual perception of Deep …

Physical adversarial attacks against deep neural networks (DNNs) have recently gained
increasing attention. The current mainstream physical attacks use printed adversarial …

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

Thermal infrared imaging is widely used in body temperature measurement, security
monitoring, and so on, but its safety research attracted attention only in recent years. We …

Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial
training (SAT). However, initial FAT encounters catastrophic overfitting, ie, the robust …

Abstract We introduce OPtical ADversarial attack (OPAD). OPAD is an adversarial attack in
the physical space aiming to fool image classifiers without physically touching the objects …