Towards Robust Training of Multi-Sensor data Fusion Network Against Adversarial Examples in Semantic Segmentation

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The success of multi-sensor data fusions in deep learning appears to be attributed to the use of complementary information among multiple sensor datasets. Compared to their predictive performance, relatively less attention has been devoted to the adversarial robustness of multi-sensor data fusion models. To achieve adversarial robust multi-sensor data fusion networks, we propose here a novel robust training scheme called Multi-Sensor Cumulative Learning (MSCL). The motivation behind the MSCL method is based on the way human beings learn new skills. The MSCL allows the multi-sensor fusion network to learn robust features from individual sensors, and then learn complex joint features from multiple sensors just as people learn to walk before they run. The step wise framework of MSCL enables the network to incorporate pre-trained knowledge of robustness with new joint information from multiple sensors. Extensive experimental evidence validated that the MSCL outperforms other multi-sensor fusion training in defending against adversarial examples.
Publisher
IEEE Signal Processing Society
Issue Date
2021-06-07
Language
English
Citation

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4710 - 4714

DOI
10.1109/ICASSP39728.2021.9413772
URI
http://hdl.handle.net/10203/281508
Appears in Collection
EE-Conference Papers(학술회의논문)
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