  LiDAR sensors are used widely in Autonomous Vehicles for better perceiving
the environment which enables safer driving decisions. Recent work has
demonstrated serious LiDAR spoofing attacks with alarming consequences. In
particular, model-level LiDAR spoofing attacks aim to inject fake depth
measurements to elicit ghost objects that are erroneously detected by 3D Object
Detectors, resulting in hazardous driving decisions. In this work, we explore
the use of motion as a physical invariant of genuine objects for detecting such
attacks. Based on this, we propose a general methodology, 3D Temporal
Consistency Check (3D-TC2), which leverages spatio-temporal information from
motion prediction to verify objects detected by 3D Object Detectors. Our
preliminary design and implementation of a 3D-TC2 prototype demonstrates very
promising performance, providing more than 98% attack detection rate with a
recall of 91% for detecting spoofed Vehicle (Car) objects, and is able to
achieve real-time detection at 41Hz
