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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MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing

Gen Zhang (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Tai Yue (National University of Defense Technology), Xiangdong Kong (National University of Defense Technology), Shan Huang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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The Inconvenient Truths of Ground Truth for Binary Analysis

Jim Alves-Foss, Varsha Venugopal (University of Idaho)

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Demystifying Local Business Search Poisoning for Illicit Drug Promotion

Peng Wang (Indiana University Bloomington), Zilong Lin (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington)

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