  It is imperative to ensure the robustness of deep learning models in critical
applications such as, healthcare. While recent advances in deep learning have
improved the performance of volumetric medical image segmentation models, these
models cannot be deployed for real-world applications immediately due to their
vulnerability to adversarial attacks. We present a 3D frequency domain
adversarial attack for volumetric medical image segmentation models and
demonstrate its advantages over conventional input or voxel domain attacks.
Using our proposed attack, we introduce a novel frequency domain adversarial
training approach for optimizing a robust model against voxel and frequency
domain attacks. Moreover, we propose frequency consistency loss to regulate our
frequency domain adversarial training that achieves a better tradeoff between
model's performance on clean and adversarial samples. Code is publicly
available at https://github.com/asif-hanif/vafa.
