  For autonomous driving, an essential task is to detect surrounding objects
accurately. To this end, most existing systems use optical devices, including
cameras and light detection and ranging (LiDAR) sensors, to collect environment
data in real time. In recent years, many researchers have developed advanced
machine learning models to detect surrounding objects. Nevertheless, the
aforementioned optical devices are vulnerable to optical signal attacks, which
could compromise the accuracy of object detection. To address this critical
issue, we propose a framework to detect and identify sensors that are under
attack. Specifically, we first develop a new technique to detect attacks on a
system that consists of three sensors. Our main idea is to: 1) use data from
three sensors to obtain two versions of depth maps (i.e., disparity) and 2)
detect attacks by analyzing the distribution of disparity errors. In our study,
we use real data sets and the state-of-the-art machine learning model to
evaluate our attack detection scheme and the results confirm the effectiveness
of our detection method. Based on the detection scheme, we further develop an
identification model that is capable of identifying up to n-2 attacked sensors
in a system with one LiDAR and n cameras. We prove the correctness of our
identification scheme and conduct experiments to show the accuracy of our
identification method. Finally, we investigate the overall sensitivity of our
framework.
