Adversarially Robust Multi-Sensor Fusion Model Training via Random Feature Fusion for Semantic Segmentation

Abstract

Multi-sensor data fusion model aims to improve the model performance by fusing multiple types of sensor data. Although multi-sensor data fusion models have been developed for remarkable performance, there is a lack of studies on the adversarial vulnerability of the multi-sensor data fusion models. In this paper, we propose a robust multi-sensor data fusion method that is not vulnerable to adversarial attacks. To this end, we devise a random feature fusion method to preserve multi-sensor fusion features. Through the random feature fusion, we could explicitly hide the information about which features are being used for the fusion. In experiments, we verify that our proposed random feature fusion method shows the adversarial robustness considerably under diverse adversarial settings.