On Frequency Domain Adversarial Vulnerabilities of Volumetric Medical Image Segmentation

In safety-critical domains like healthcare, resilience of deep learning models towards adversarial attacks is crucial. Volumetric medical image segmentation is a fundamental task, providing critical insights for diagnosis. This paper introduces a novel frequency domain adversarial attack targeting 3D medical data, revealing vulnerabilities in segmentation models. By manipulating the frequency spectrum (low, middle, and high bands), we assess its impact on model performance. Unlike pixel-based 2D attacks, our method continuously perturbs 3D samples with minimal information loss, achieving high fooling rates at lower computational costs and superior black-box transferability, while maintaining perceptual quality. Our code is publicly available at Github<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup>https://github.com/asif-hanif/saa.