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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Remote Memory-Deduplication Attacks

Martin Schwarzl (Graz University of Technology), Erik Kraft (Graz University of Technology), Moritz Lipp (Graz University of Technology), Daniel Gruss (Graz University of Technology)

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ScriptChecker: To Tame Third-party Script Execution With Task Capabilities

Wu Luo (Peking University), Xuhua Ding (Singapore Management University), Pengfei Wu (School of Computing, National University of Singapore), Xiaolei Zhang (Peking University), Qingni Shen (Peking University), Zhonghai Wu (Peking University)

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Generating Test Suites for GPU Instruction Sets through Mutation...

Shoham Shitrit(University of Rochester) and Sreepathi Pai (University of Rochester)

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NC-Max: Breaking the Security-Performance Tradeoff in Nakamoto Consensus

Ren Zhang (Nervos), Dingwei Zhang (Nervos), Quake Wang (Nervos), Shichen Wu (School of Cyber Science and Technology, Shandong University), Jan Xie (Nervos), Bart Preneel (imec-COSIC, KU Leuven)

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