LiDAR point clouds collected from a moving vehicle are functions of its
trajectories, because the sensor motion needs to be compensated to avoid
distortions. When autonomous vehicles are sending LiDAR point clouds to deep
networks for perception and planning, could the motion compensation
consequently become a wide-open backdoor in those networks, due to both the
adversarial vulnerability of deep learning and GPS-based vehicle trajectory
estimation that is susceptible to wireless spoofing? We demonstrate such
possibilities for the first time: instead of directly attacking point cloud
coordinates which requires tampering with the raw LiDAR readings, only
adversarial spoofing of a self-driving car's trajectory with small
perturbations is enough to make safety-critical objects undetectable or
detected with incorrect positions. Moreover, polynomial trajectory perturbation
is developed to achieve a temporally-smooth and highly-imperceptible attack.
Extensive experiments on 3D object detection have shown that such attacks not
only lower the performance of the state-of-the-art detectors effectively, but
also transfer to other detectors, raising a red flag for the community. The
code is available on this https URL

通过对无人车的轨迹进行微小干扰，恶意攻击者能够对深度学习网络中的运动补偿机制进行攻击，破坏无人车对安全关键物体的探测。多项实验表明，这种攻击手段不仅能够有效降低现有最优检测器的性能，而且还具有较强的通用性。