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

In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …

In autonomous driving (AD), accurate perception is indispensable to achieving safe and
secure driving. Due to its safety-criticality, the security of AD perception has been widely …

Deep learning-based monocular depth estimation (MDE) extensively applied in autonomous
driving is known to be vulnerable to adversarial attacks. Previous physical attacks against …

To perform adversarial attacks in the physical world, many studies have proposed
adversarial camouflage, a method to hide a target object by applying camouflage patterns …

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

Recent works have proposed to craft adversarial clothes for evading person detectors, while
they are either only effective at limited viewing angles or very conspicuous to humans. We …

Adversarial camouflage has garnered attention for its ability to attack object detectors from
any viewpoint by covering the entire object's surface. However, universality and robustness …

Deep Neural Networks (DNNs) have shown impressive performance in computer vision
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …

Adversarial attacks on thermal infrared imaging expose the risk of related applications.
Estimating the security of these systems is essential for safely deploying them in the real …