Semantic segmentation of hyperspectral images (HSI) has seen great strides in recent years by incorporating knowledge from deep learning RGB classification models. Similar to their classification counterparts, semantic segmentation models are vulnerable to adversarial examples and need adversarial training to counteract them. 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 the 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. Our approach allows for the presence of multiple attacks mixed together while also labeling attack types during testing. We experimentally show that ADE-Net outperforms the baseline, which is a single network adversarially trained under a mix of multiple attacks, for HSI Indian Pines, Kennedy Space, and Houston datasets.
翻译:近些年来,超光谱图像(HSI)的超光谱分解(SHI)通过吸收深层学习的 RGB 分类模型的知识取得了长足的进步。与其分类模型类似,语系分解模型很容易成为对抗性实例,需要对抗性培训。传统对抗性稳健方法侧重于对一个关于被攻击数据的单一网络进行培训或再培训,然而,在多次攻击的情况下,这些方法降低了与对每次攻击分别培训的网络相比的性能。为了解决这一问题,我们提议建立一个反向分解器网络(ADE-Net),侧重于攻击型探测和对抗性强力,并采用统一模型,以优化保存数据型重量,同时对整个网络进行稳健。在拟议方法中,使用歧视网络将按攻击类型分列的数据分别纳入其具体的攻击-专家共通网络。我们的方法允许在试验期间同时出现多重攻击组合,同时标注攻击类型。我们实验性地表明,ADE-Net超越了基线,这是在一次多次攻击的混合下,对HSI Indian Prestions、 Stostone and Strius and data and data.