Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote

Abstract

The lack of robustness shown by deep neural networks (DNNs) questions their deployment in safety-critical tasks, such as autonomous driving. We pick up the recently introduced redundant teacher-student frameworks (3 DNNs) and propose in this work a novel error detection and correction scheme with application to semantic segmentation. It obtains its robustnesss by an online-adapted and therefore hard-to-attack student DNN during vehicle operation, which builds upon a novel layer-dependent inverse feature matching (IFM) loss. We conduct experiments on the Cityscapes dataset showing that this loss renders the adaptive student to be more than 20% absolute mean intersection-over-union (mIoU) better than in previous works. Moreover, the entire error correction virtually always delivers the performance of the best non-attacked network, resulting in an mIoU of about 50% even under strongest attacks (instead of 1...2%), while keeping the performance on clean data at about original level (ca. 75.7%).