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