Evaluation of Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric Study

The study explores the vulnerability of semantic segmentation models to adversarial input perturbations in the domain of off-road autonomous driving. Existing studies have primarily concentrated on enhancing model's robustness via architectural modifications along-with using noisy images during training. On the contrary, little attention has been paid to investigating the impact of datasets on the adversarial attacks. Our study aims to address this gap by examining the impact of non-robust features in off-road datasets and comparing the effects of adversarial attacks on different segmentation network architectures. To enable this, a robust dataset is created consisting of only robust features and training the networks on this robustified dataset. We present both qualitative and quantitative analysis of our findings. The code is publicly available at https:// github.com/ rohtkumar/ adversarial_attacks_ on_segmentation