4 papers
UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
Jingyu Zhang, Jacky Wai Keung, Yan Xiao +3
Adversarial attacks play a pivotal role in testing and improving the reliability of deep learning (DL) systems. Existing literature has demonstrated that subtle perturbations to th…
Empirical Insights of Test Selection Metrics under Multiple Testing Objectives and Distribution Shifts
Jingyu Zhang, Fan Wang, Jacky Keung +3
Deep learning (DL)-based systems can exhibit unexpected behavior when exposed to out-of-distribution (OOD) scenarios, posing serious risks in safety-critical domains such as malwar…
Towards Stealthy and Effective Backdoor Attacks on Lane Detection: A Naturalistic Data Poisoning Approach
Yifan Liao, Yuxin Cao, Yedi Zhang +5
Deep learning-based lane detection (LD) plays a critical role in autonomous driving and advanced driver assistance systems. However, its vulnerability to backdoor attacks presents…
Towards Powerful and Practical Patch Attacks for 2D Object Detection in Autonomous Driving
Yuxin Cao, Yedi Zhang, Wentao He +5
Learning-based autonomous driving systems remain critically vulnerable to adversarial patches, posing serious safety and security risks in their real-world deployment. Black-box at…