SLAMSpoof：スキャンマッチング脆弱性解析に基づく測位システムへの実用的LiDARスプーフィング攻撃
SLAMSpoof: Practical LiDAR Spoofing Attacks on Localization Systems Guided by Scan Matching Vulnerability Analysis
LiDAR測位システムに対する初の実用的なスプーフィング攻撃手法を提案し、スキャンマッチングの脆弱性スコアで攻撃位置を特定、実車実験で4.2m以上の測位誤差を誘発できることを示した。
著者: Rokuto Nagata, Kenji Koide, Yuki Hayakawa, Ryo Suzuki, Kazuma Ikeda, Ozora Sako, Qi Alfred Chen, Takami Sato, Kentaro Yoshioka
分類: cs.RO
原文アブストラクト
Accurate localization is essential for enabling modern full self-driving services. These services heavily rely on map-based traffic information to reduce uncertainties in recognizing lane shapes, traffic light locations, and traffic signs. Achieving this level of reliance on map information requires centimeter-level localization accuracy, which is currently only achievable with LiDAR sensors. However, LiDAR is known to be vulnerable to spoofing attacks that emit malicious lasers against LiDAR to overwrite its measurements. Once localization is compromised, the attack could lead the victim off roads or make them ignore traffic lights. Motivated by these serious safety implications, we design SLAMSpoof, the first practical LiDAR spoofing attack on localization systems for self-driving to assess the actual attack significance on autonomous vehicles. SLAMSpoof can effectively find the effective attack location based on our scan matching vulnerability score (SMVS), a point-wise metric representing the potential vulnerability to spoofing attacks. To evaluate the effectiveness of the attack, we conduct real-world experiments on ground vehicles and confirm its high capability in real-world scenarios, inducing position errors of $\geq$4.2 meters (more than typical lane width) for all 3 popular LiDAR-based localization algorithms. We finally discuss the potential countermeasures of this attack. Code is available at https://github.com/Keio-CSG/slamspoof
