  There are increasing concerns about malicious attacks on autonomous vehicles.
In particular, inaudible voice command attacks pose a significant threat as
voice commands become available in autonomous driving systems. How to
empirically defend against these inaudible attacks remains an open question.
Previous research investigates utilizing deep learning-based multimodal fusion
for defense, without considering the model uncertainty in trustworthiness. As
deep learning has been applied to increasingly sensitive tasks, uncertainty
measurement is crucial in helping improve model robustness, especially in
mission-critical scenarios. In this paper, we propose the Multimodal Fusion
Framework (MFF) as an intelligent security system to defend against inaudible
voice command attacks. MFF fuses heterogeneous audio-vision modalities using
VGG family neural networks and achieves the detection accuracy of 92.25% in the
comparative fusion method empirical study. Additionally, extensive experiments
on audio-vision tasks reveal the model's uncertainty. Using Expected
Calibration Errors, we measure calibration errors and Monte-Carlo Dropout to
estimate the predictive distribution for the proposed models. Our findings show
empirically to train robust multimodal models, improve standard accuracy and
provide a further step toward interpretability. Finally, we discuss the pros
and cons of our approach and its applicability for Advanced Driver Assistance
Systems.
