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.
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Acknowledgements
This work was supported by A*STAR Advanced Manufacturing and Engineering (AME) Programmatic Fund (A20H4b0141);
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Le, L.D. et al. (2022). An Efficient Defending Mechanism Against Image Attacking on Medical Image Segmentation Models. In: Xu, X., Li, X., Mahapatra, D., Cheng, L., Petitjean, C., Fu, H. (eds) Resource-Efficient Medical Image Analysis. REMIA 2022. Lecture Notes in Computer Science, vol 13543. Springer, Cham. https://doi.org/10.1007/978-3-031-16876-5_7
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