PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image
  Deraining for Semantic Segmentation
Xianghao Jiao Yaohua Liu Jiaxin Gao Xinyuan Chu Risheng Liu Xin Fan
In light of the significant progress made in the development and application
of semantic segmentation tasks, there has been increasing attention towards
improving the robustness of segmentation models against natural degradation
factors (e.g., rain streaks) or artificially attack factors (e.g., adversarial
attack). Whereas, most existing methods are designed to address a single
degradation factor and are tailored to specific application scenarios. In this
work, we present the first attempt to improve the robustness of semantic
segmentation tasks by simultaneously handling different types of degradation
factors. Specifically, we introduce the Preprocessing Enhanced Adversarial
Robust Learning (PEARL) framework based on the analysis of our proposed Naive
Adversarial Training (NAT) framework. Our approach effectively handles both
rain streaks and adversarial perturbation by transferring the robustness of the
segmentation model to the image derain model. Furthermore, as opposed to the
commonly used Negative Adversarial Attack (NAA), we design the Auxiliary Mirror
Attack (AMA) to introduce positive information prior to the training of the
PEARL framework, which improves defense capability and segmentation
performance. Our extensive experiments and ablation studies based on different
derain methods and segmentation models have demonstrated the significant
performance improvement of PEARL with AMA in defense against various
adversarial attacks and rain streaks while maintaining high generalization
performance across different datasets.