AI RESEARCH
UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
arXiv CS.LG
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ArXi:2604.23362v1 Announce Type: cross Adversarial attacks play a pivotal role in testing and improving the reliability of deep learning (DL) systems. Existing literature has nstrated that subtle perturbations to the input can elicit erroneous outcomes, thereby substantially compromising the security of DL systems. This has emerged as a critical concern in the development of DL-based safety-critical systems like Autonomous Driving Systems (ADSs