  LiDAR sensors are widely used in autonomous vehicles to better perceive the
environment. However, prior works have shown that LiDAR signals can be spoofed
to hide real objects from 3D object detectors. This study explores the
feasibility of reducing the required spoofing area through a novel
object-hiding strategy based on virtual patches (VPs). We first manually design
VPs (MVPs) and show that VP-focused attacks can achieve similar success rates
with prior work but with a fraction of the required spoofing area. Then we
design a framework Saliency-LiDAR (SALL), which can identify critical regions
for LiDAR objects using Integrated Gradients. VPs crafted on critical regions
(CVPs) reduce object detection recall by at least 15% compared to our baseline
with an approximate 50% reduction in the spoofing area for vehicles of average
size.
