Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems

TL;DR

This paper presents a novel framework for detecting and identifying optical signal attacks on sensors used in autonomous driving, enhancing system robustness against malicious interference.

Contribution

The authors introduce a new attack detection and sensor identification framework leveraging disparity error analysis and prove its correctness with experimental validation.

Findings

Effective attack detection using disparity error distribution

Ability to identify up to n-2 attacked sensors

Validated with real datasets and state-of-the-art models

Abstract

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.