  Semantic segmentation models classifying hyperspectral images (HSI) are
vulnerable to adversarial examples. Traditional approaches to adversarial
robustness focus on training or retraining a single network on attacked data,
however, in the presence of multiple attacks these approaches decrease in
performance compared to networks trained individually on each attack. To combat
this issue we propose an Adversarial Discriminator Ensemble Network (ADE-Net)
which focuses on attack type detection and adversarial robustness under a
unified model to preserve per data-type weight optimally while robustifiying
the overall network. In the proposed method, a discriminator network is used to
separate data by attack type into their specific attack-expert ensemble
network.
