AI RESEARCH
Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving
arXiv CS.CV
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ArXi:2604.23105v1 Announce Type: new Deep learning drives major advances in autonomous driving (AD), where object detectors are central to perception. However, adversarial attacks pose significant threats to the reliability and safety of these systems, with physical adversarial patches representing a particularly potent form of attack. Physical adversarial patch attacks pose severe risks but are usually crafted for a single model, yielding poor transferability to unseen detectors. We propose AdvAD, a transfer-based physical attack against object detection in autonomous driving.