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LiDAR sensors are used widely in Autonomous Vehicles for better perceivingthe environment which enables safer driving decisions. Recent work hasdemonstrated serious LiDAR spoofing attacks with alarming consequences. Inparticular, model-level LiDAR spoofing attacks aim to inject fake depthmeasurements to elicit ghost objects that are erroneously detected by 3D ObjectDetectors, resulting in hazardous driving decisions. In this work, we explorethe use of motion as a physical invariant of genuine objects for detecting suchattacks. Based on this, we propose a general methodology, 3D TemporalConsistency Check (3D-TC2), which leverages spatio-temporal information frommotion prediction to verify objects detected by 3D Object Detectors. Ourpreliminary design and implementation of a 3D-TC2 prototype demonstrates verypromising performance, providing more than 98% attack detection rate with arecall of 91% for detecting spoofed Vehicle (Car) objects, and is able toachieve real-time detection at 41Hz

Temporal Consistency Checks to Detect LiDAR Spoofing Attacks on Autonomous Vehicle Perception