Adversarial Objects Against LiDAR-Based Autonomous Driving Systems

11 Jul 2019Yulong CaoChaowei XiaoDawei YangJing FangRuigang YangMingyan LiuBo Li

Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions. Recent studies show that adversarial examples can pose a threat to real-world security-critical applications: a "physical adversarial Stop Sign" can be synthesized such that the autonomous driving cars will misrecognize it as others (e.g., a speed limit sign)... (read more)

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