Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Deep neural networks (DNNs) have been increasingly used in face recognition (FR)
systems. Recent studies, however, show that DNNs are vulnerable to adversarial examples …

Autonomous Vehicles (AVs) are closely connected in the Cooperative Intelligent
Transportation System (C-ITS). They are equipped with various sensors and controlled by …

Adversarial patch attacks are among the most practical adversarial attacks. Recent efforts
focus on providing a certifiable guarantee on correct predictions in the presence of white …

Although Deep Neural Networks (DNNs) have been widely applied in various real-world
scenarios, they are vulnerable to adversarial examples. The current adversarial attacks in …

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

As Face Recognition (FR) technology becomes increasingly prevalent in finance, the
military, public safety, and everyday life, security concerns have grown substantially …

Adversarial patch attacks that craft the pixels in a confined region of the input images show
their powerful attack effectiveness in physical environments even with noises or …

Recently, a significant boom has been noticed in the generation of a variety of malicious
examples ranging from adversarial perturbations to common noises to natural adversaries …

DNN-based video object detection (VOD) powers autonomous driving and video
surveillance industries with rising importance and promising opportunities. However …