Bo Yang (Zhejiang University), Yushi Cheng (Tsinghua University), Zizhi Jin (Zhejiang University), Xiaoyu Ji (Zhejiang University) and Wenyuan Xu (Zhejiang University)

Due to the booming of autonomous driving, in which LiDAR plays a critical role in the task of environment perception, its reliability issues have drawn much attention recently. LiDARs usually utilize deep neural models for 3D point cloud perception, which have been demonstrated to be vulnerable to imperceptible adversarial examples. However, prior work usually manipulates point clouds in the digital world without considering the physical working principle of the actual LiDAR. As a result, the generated adversarial point clouds may be realizable and effective in simulation but cannot be perceived by physical LiDARs. In this work, we introduce the physical principle of LiDARs and propose a new method for generating 3D adversarial point clouds in accord with it that can achieve two types of spoofing attacks: object hiding and object creating. We also evaluate the effectiveness of the proposed method with two 3D object detectors on the KITTI vision benchmark.

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Weikeng Chen (DZK/UC Berkeley), Thang Hoang (Virginia Tech), Jorge Guajardo (Robert Bosch Research and Technology Center), Attila A. Yavuz (University...

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Phishing awareness and education – When to best remind?

Benjamin Maximilian Berens (SECUSO, Karlsruhe Institute of Technology), Katerina Dimitrova, Mattia Mossano (SECUSO, Karlsruhe Institute of Technology), Melanie Volkamer (SECUSO,...

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Problematic Content in Online Ads

Franzisca Roesner (University of Washington)

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Chunked-Cache: On-Demand and Scalable Cache Isolation for Security Architectures

Ghada Dessouky (Technical University of Darmstadt), Emmanuel Stapf (Technical University of Darmstadt), Pouya Mahmoody (Technical University of Darmstadt), Alexander Gruler...

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