Robust Sensor Fusion Algorithms Against VoiceCommand Attacks in Autonomous Vehicles

• URL: http://arxiv.org/abs/2104.09872v1
• Date: Tue, 20 Apr 2021 10:08:46 GMT
• Title: Robust Sensor Fusion Algorithms Against VoiceCommand Attacks in Autonomous Vehicles
• Authors: Jiwei Guan, Xi Zheng, Chen Wang, Yipeng Zhou and Alireza Jolfa
• Abstract summary: We propose a novel multimodal deep learning classification system to defend against inaudible command attacks. Our experimental results confirm the feasibility of the proposed defense methods and the best classification accuracy reaches 89.2%.
• Score: 8.35945218644081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With recent advances in autonomous driving, Voice Control Systems have become increasingly adopted as human-vehicle interaction methods. This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS). Prior work has shown that Siri, Alexa and Cortana, are highly vulnerable to inaudible command attacks. This could be extended to ADAS in real-world applications and such inaudible command threat is difficult to detect due to microphone nonlinearities. In this paper, we aim to develop a more practical solution by using camera views to defend against inaudible command attacks where ADAS are capable of detecting their environment via multi-sensors. To this end, we propose a novel multimodal deep learning classification system to defend against inaudible command attacks. Our experimental results confirm the feasibility of the proposed defense methods and the best classification accuracy reaches 89.2%. Code is available at https://github.com/ITSEG-MQ/Sensor-Fusion-Against-VoiceCommand-Attacks.

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