Capraru, R., Lupu, E. C., Wang, J.-G., & Soong, B. H. (2025). GhostLite: Data Minimization with Applications to Real-Time LiDAR Attacks. 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall), 1–7. https://doi.org/10.1109/vtc2025-fall65116.2025.11310068
Abstract:
LiDAR-based object detection plays a crucial role
in autonomous driving, yet remains vulnerable to ghost object
attacks, where adversarially crafted point clouds trigger false
detections. Traditional attack methods often require a large
number of points and substantial computational resources,
making them inapplicable in real-world scenarios. Although
prior research has explored reducing the number of required
attack points, real-world feasibility remains a challenge due to
the high computational cost and attack generation time. While
LiDAR object detection can be compromised given enough
adversarially crafted points, we believe there are some critical
points, which we call reduced attack budget, which are sufficient
to attack the detector. Motivated by this consideration, we
propose a novel geometric- and heuristic-based approach capable
of generating effective ghost object attacks by leveraging
the target’s contour characteristics. By strategically selecting
a minimal set of adversarial points, as few as 20 points, our
approach maintains high detection confidence, reduces attack
execution time and attack budget by up to 4 and 73 times,
respectively, in this paper. Our method has been verified on both
simulated and real-world datasets (KITTI). The experimental
results demonstrate that our optimized attack significantly
reduces computational overhead and consequently improves the
attack’s real-time feasibility.
License type:
Publisher Copyright
Funding Info:
Ministry of Education Academic Research Fund Tier 1