An Efficient Defending Mechanism Against Image Attacking on Medical Image Segmentation Models

Linh D. Le, Huazhu Fu, Xinxing Xu, Yong Liu, Yanyu Xu, Jiawei Du, Joey Tianyi Zhou, Rick Siow Mong Goh

Abstract: Image attacking has been studied for a long time. However, in reality, the number of research on defending against the attacks on segmentation models is still limited especially for medical imaging. To fill this research gap, we propose a novel defending mechanism against adversarial attacks for the segmentation models. We focus on segmentation as robustness improvement on segmentation is much more challenging due to its dense nature, and segmentation is at the center of medical imaging tasks. In this paper, we are the first time to employ Transformer as a technique to protect the segmentation models from attacks. Our result on several medical well-known benchmark datasets shows that the proposed defending mechanism to enhance the segmentation models is effective with high scores and better compared to other strong methods.