On Adversarial Robustness of Semantic Segmentation Models for Automated Driving
Huilin Yin, Ruining Wang, Boyu Liu, Jun Yan · 2022 IEEE Intelligent Vehicles Symposium (IV) · 2022
Several research works have been proposed to evaluate the robustness of deep-learning-based semantic segmentation models for automated driving under adversarial attacks. However, the types of tested adversarial examples and evaluated segmentation models are limited in the previous empirical studies, which imposes the restrictions on the understanding of the robustness of semantic segmentation models. To alleviate these problems, we would promote the research on the robustness of semantic segmentation models systematically from two aspects of influence factors: internal factors of model structures and external factors of environmental perturbations at the data level. In this paper, we provide a comprehensive study using these typical models with different internal structures: Fully Convolutional Networks (FCN), Pyramid Scene Parsing Network (PSPNet), DeepLabV3+, and SegNet with different backbones. These models would be evaluated on the metrics of robustness under both white-box attacks and black-box attacks. Based on our experiment, we make qualitative and quantitative analyses of the robustness of diverse models under different influence factors. With more empirical study cases, our work gives inspiration to the robustness study of semantic segmentation for automated driving which is meaningful and beneficial to the safety of the intended functionality (SOTIF).