LiDAR Data Integrity Verification for Autonomous Vehicle

Deterministic perception of the surrounding environment is both crucial and a challenging task for autonomous vehicles. A wide range of sensors, including LiDAR, RADAR, cameras, and so on, are used to build the perception layer of an autonomous vehicle. Many interfaces, such as OBD-II, Wi-Fi, Bluetooth, cellular networks, etc., have been introduced in autonomous vehicles to control various functionalities, including V2X communications, over-the-air updates, security, remote vehicle-health monitoring, and so on. These interfaces are introducing new attack surfaces that can be exploited via external as well as internal attacks. Attackers have successfully demonstrated how to exploit these attack surfaces by crafting attack vectors to launch both insider and external attacks. The sensor and sensor data are also vulnerable to both external and insider attacks. Developing safeguards against these attacks is a steppingstone toward the design and development of reliable autonomous vehicles. For instance, failure to detect and localize sensor data tampering can result in an erroneous perception of the environment and lead to wrong path-planning and control decisions. In this paper, we propose a novel semi-fragile data hiding-based technique for real-time sensor data integrity verification and tamper detection and localization. Specifically, the proposed data hiding-based method relies on 3-dimensional quantization index modulation (QIM)-based data hiding to insert a binary watermark into the LiDAR data at the sensing layer, which is used for integrity verification and tamper detection and localization at the decision-making unit, e.g., the advanced driver assistance system (ADAS). The performance of the proposed scheme is evaluated on a benchmarking LiDAR dataset. The impact of information hiding on the object-recognition algorithm is also evaluated. Experimental results indicate that the proposed method can successfully detect and localize data tampering attacks, such as fake object insertion (FOI) and target object deletion (TOD). Robustness to noise-addition attacks is also evaluated.