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

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 …

In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite
various prior studies on its security issues, all of them only consider attacks on camera-or …

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

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

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial
examples, raising broad security concerns about their applications. Besides the attacks in …

In this paper we address the vulnerabilities of iris recognition systems to both image-based
impersonation attacks and Presentation Attacks (PAs) in physical environments. While …

As physical adversarial attacks become extensively applied in unearthing the potential risk
of security-critical scenarios, especially in dynamic scenarios, their vulnerability to …

In the physical world, the interplay of light and shadow can significantly impact the
performance of deep neural networks (DNNs), leading to substantial consequences, as …

Adversarial attacks can mislead deep learning models to make false predictions by
implanting small perturbations to the original input that are imperceptible to the human eye …