Ryo Suzuki (Keio University), Takami Sato (University of California, Irvine), Yuki Hayakawa, Kazuma Ikeda, Ozora Sako, Rokuto Nagata (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

LiDAR (Light Detection and Ranging) is an essential sensor for autonomous driving (AD), increasingly being integrated not only in prototype vehicles but also in commodity vehicles. Due to its critical safety implications, recent studies have explored its security risks and exposed the potential vulnerability against LiDAR spoofing attacks, which manipulate measurement data by emitting malicious lasers into the LiDAR. Nevertheless, deploying LiDAR spoofing attacks against driving AD vehicles still has significant technical challenges particularly in accurately aiming at the LiDAR of a moving AV from the roadside. The current state-of-the-art attack can be successful only at ≤5 km/h. Motivated by this, we design novel tracking and aiming methodology and conduct a feasibility study to explore the actual practicality of LiDAR spoofing attacks against AD vehicles at cruising speeds. In this work, we report our initial results demonstrating that our object removal attack successfully makes the targeted pedestrian undetectable with ≥90% success rates in a real-world scenario where the adversary at the roadside attacks the victim AD approaching at 35 km/h. Finally, we discuss the current challenges and our future plans.

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Aozhuo Sun (Institute of Information Engineering, Chinese Academy of Sciences), Jingqiang Lin (School of Cyber Science and Technology, University of Science and Technology of China), Wei Wang (Institute of Information Engineering, Chinese Academy of Sciences), Zeyan Liu (The University of Kansas), Bingyu Li (School of Cyber Science and Technology, Beihang University), Shushang Wen (School of…

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu (Indiana University Bloominton), Di Tang (Indiana University Bloomington), Siyuan Tang (Indiana University Bloomington), Zihao Wang (Indiana University Bloomington), Guanhong Tao (Purdue University), Shiqing Ma (University of Massachusetts Amherst), XiaoFeng Wang (Indiana University Bloomington), Haixu Tang (Indiana University, Bloomington)

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Samuel Jero (MIT Lincoln Laboratory), Juliana Furgala (MIT Lincoln Laboratory), Max A Heller (MIT Lincoln Laboratory), Benjamin Nahill (MIT Lincoln Laboratory), Samuel Mergendahl (MIT Lincoln Laboratory), Richard Skowyra (MIT Lincoln Laboratory)

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