论文标题
在多次攻击下改善高光谱的对抗鲁棒性
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks
论文作者
论文摘要
分类高光谱图像（HSI）的语义分割模型容易受到对抗示例的影响。与在攻击数据的情况下，与对每次攻击单独训练的网络相比，在有多种攻击的情况下，传统的鲁棒性方法专注于训练或在受到攻击数据上进行单个网络的培训。为了解决这个问题，我们提出了一个对抗性歧视器集合网络（ADE-NET），该集合网络着重于统一模型下的攻击类型检测和对抗性鲁棒性，以最佳地保留每个数据型重量，同时稳健地列出整体网络。在提出的方法中，歧视网络用于通过攻击类型中的特定攻击 - 专家集成网络将数据分开。
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.
