TileMask: A Passive-Reflection-based Attack against mmWave Radar Object Detection in Autonomous Driving

In autonomous driving, millimeter wave (mmWave) radar has been widely adopted for object detection because of its robustness and reliability under various weather and lighting conditions. For radar object detection, deep neural networks (DNNs) are becoming increasingly important because they are more robust and accurate, and can provide rich semantic information about the detected objects, which is critical for autonomous vehicles (AVs) to make decisions. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Despite the rapid development of DNN-based radar object detection models, there have been no studies on their vulnerability to adversarial attacks. Although some spoofing attack methods are proposed to attack the radar sensor by actively transmitting specific signals using some special devices, these attacks require sub-nanosecond-level synchronization between the devices and the radar and are very costly, which limits their practicability in real world. In addition, these attack methods can not effectively attack DNN-based radar object detection. To address the above problems, in this paper, we investigate the possibility of using a few adversarial objects to attack the DNN-based radar object detection models through passive reflection. These objects can be easily fabricated using 3D printing and metal foils at low cost. By placing these adversarial objects at some specific locations on a target vehicle, we can easily fool the victim AV's radar object detection model. The experimental results demonstrate that the attacker can achieve the attack goal by using only two adversarial objects and conceal them as car signs, which have good stealthiness and flexibility. To the best of our knowledge, this is the first study on the passive-reflection-based attacks against the DNN-based radar object detection models using low-cost, readily-available and easily concealable geometric shaped objects.