Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images
Journal:
arXiv
Published Date:
May 3, 2025
Abstract
Inland water body segmentation from Synthetic Aperture Radar (SAR) images is
an important task needed for several applications, such as flood mapping. While
SAR sensors capture data in all-weather conditions as high-resolution images,
differentiating water and water-like surfaces from SAR images is not
straightforward. Inland water bodies, such as large river basins, have complex
geometry, which adds to the challenge of segmentation. U-Net is a widely used
deep learning model for land-water segmentation of SAR images. In practice,
manual annotation is often used to generate the corresponding water masks as
ground truth. Manual annotation of the images is prone to label noise owing to
data poisoning attacks, especially due to complex geometry. In this work, we
simulate manual errors in the form of adversarial attacks on the U-Net model
and study the robustness of the model to human errors in annotation. Our
results indicate that U-Net can tolerate a certain level of corruption before
its performance drops significantly. This finding highlights the crucial role
that the quality of manual annotations plays in determining the effectiveness
of the segmentation model. The code and the new dataset, along with adversarial
examples for robust training, are publicly available. (GitHub link -
https://github.com/GVCL/IWSeg-SAR-Poison.git)
