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LiDAR-driven 3D sensing allows new generations of vehicles to achieveadvanced levels of situation awareness. However, recent works have demonstratedthat physical adversaries can spoof LiDAR return signals and deceive 3D objectdetectors to erroneously detect "ghost" objects. Existing defenses are eitherimpractical or focus only on vehicles. Unfortunately, it is easier to spoofsmaller objects such as pedestrians and cyclists, but harder to defend againstand can have worse safety implications. To address this gap, we introduceShadow-Catcher, a set of new techniques embodied in an end-to-end prototype todetect both large and small ghost object attacks on 3D detectors. Wecharacterize a new semantically meaningful physical invariant (3D shadows)which Shadow-Catcher leverages for validating objects. Our evaluation on theKITTI dataset shows that Shadow-Catcher consistently achieves more than 94%accuracy in identifying anomalous shadows for vehicles, pedestrians, andcyclists, while it remains robust to a novel class of strong "invalidation"attacks targeting the defense system. Shadow-Catcher can achieve real-timedetection, requiring only between 0.003s-0.021s on average to process an objectin a 3D point cloud on commodity hardware and achieves a 2.17x speedup comparedto prior work

Shadow-Catcher: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing