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

LiDAR stands as a critical sensor in the realm of autonomous vehicles (AVs). Considering its safety and security criticality, recent studies have actively researched its security and warned of various safety implications against LiDAR spoofing attacks, which can cause critical safety implications on AVs by injecting ghost objects or removing legitimate objects from their detection. To defend against LiDAR spoofing attacks, pulse fingerprinting has been expected as one of the most promising countermeasures against LiDAR spoofing attacks, and recent research demonstrates its high defense capability, especially against object removal attacks. In this WIP paper, we report the progress in conducting further security analysis on pulse fingerprinting against LiDAR spoofing attacks. We design a novel adaptive attack strategy, the Adaptive High-Frequency Removal (A-HFR) attack, which can be effective against broader types of LiDARs than the existing HFR attacks. We evaluate the A-HFR attack on three commercial LiDAR with pulse fingerprinting and find that the A-HFR attack can successfully remove over 96% of the point cloud within a 20◦ horizontal and a 16◦ vertical angle. Our finding indicates that current pulse fingerprinting techniques might not be sufficiently robust to thwart spoofing attacks. We also discuss potential strategies to enhance the defensive efficacy of pulse fingerprinting against such attacks. This finding implies that the current pulse fingerprinting may not be an ultimate countermeasure against LiDAR spoofing attacks. We finally discuss our future plans.

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