  This paper presents a novel yet efficient defense framework for segmentation
models against adversarial attacks in medical imaging. In contrary to the
defense methods against adversarial attacks for classification models which
widely are investigated, such defense methods for segmentation models has been
less explored. Our proposed method can be used for any deep learning models
without revising the target deep learning models, as well as can be independent
of adversarial attacks. Our framework consists of a frequency domain converter,
a detector, and a reformer. The frequency domain converter helps the detector
detects adversarial examples by using a frame domain of an image. The reformer
helps target models to predict more precisely. We have experiments to
empirically show that our proposed method has a better performance compared to
the existing defense method.
