Deep learning in image reconstruction: vulnerability under adversarial attacks and potential defense strategies

Chengzhu Zhang; Yinsheng Li; Guang-Hong Chen

doi:10.1117/12.2581369

15 February 2021 Deep learning in image reconstruction: vulnerability under adversarial attacks and potential defense strategies

Chengzhu Zhang, Yinsheng Li, Guang-Hong Chen

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115951U (2021) https://doi.org/10.1117/12.2581369
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

In this work, we studied the adversarial attacks and their corresponding defense strategies specifically in x-ray computed tomography image reconstruction tasks. After a small amount of imperceptible noise was added to the input image, these barely noticeable additional noise to the input image resulted in artifactual false-positive structures into output images of the well referenced high performance deep learning reconstruction methods. Since the adversarial attacks often occur at a specific stage of the entire imaging chain, defense measures can be developed to incorporate the uncontaminated data in the imaging chain into the image reconstruction framework to eliminate hazardous effects of adversarial attacks.

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Chengzhu Zhang, Yinsheng Li, and Guang-Hong Chen "Deep learning in image reconstruction: vulnerability under adversarial attacks and potential defense strategies", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115951U (15 February 2021); https://doi.org/10.1117/12.2581369

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