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.

翻译：对高光谱图像进行分类的语义分割模型容易受到对抗样本的攻击。传统的对抗鲁棒性方法侧重于在受攻击数据上训练或重新训练单个网络，然而，当面临多种攻击时，与针对每种攻击分别训练的网络相比，这些方法的性能会下降。为解决这一问题，我们提出了一种对抗判别器集成网络（ADE-Net），该网络专注于攻击类型检测和统一模型下的对抗鲁棒性，从而在增强整体网络鲁棒性的同时，最优地保持每种数据类型的权重。在所提出的方法中，一个判别器网络被用于根据攻击类型将数据分离到其特定的攻击专家集成网络中。