Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles

TL;DR

This paper proposes a multimodal deep learning system combining camera and sensor data to defend autonomous vehicle voice command systems against inaudible attack threats, achieving up to 89.2% accuracy.

Contribution

It introduces a novel sensor fusion approach for detecting inaudible voice command attacks in autonomous vehicles, enhancing security and robustness.

Findings

Achieved up to 89.2% classification accuracy.

Demonstrated feasibility of multimodal defense against inaudible attacks.

Provided open-source code for the proposed system.

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.