Deep Sensor Fusion for Detection and Localization of Automotive Radar Spoofing Attacks

Radar is a key sensing modality to obtain scene understanding in automotive driving applications. However, automotive radars that are based on the widely used frequency-modulated continuous-wave (FMCW) signal waveforms are vulnerable to signal spoofing attacks. These attacks can distort the resulting radar images at the victim radar receiver via a false positive by introducing artificial objects, or due to a false negative by concealing real objects. In this work, we propose a deep learning method to detect radar points compromised by spoofing attacks and to classify the type of attack. Leveraging mid-fusion of radar and camera sensors, as well as a multi-head architecture, our method simultaneously identifies the specific spatial locations where spoofing attacks distort the radar image, which enables the use of undistorted portions of the data for partial functionality. Evaluation on real data with simulated cases of spoofing attacks shows that our approach achieves mean classification F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores of 0.93 for false positive and 0.82 for false negative attacks, demonstrating its effectiveness in enhancing radar image integrity.