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The Security of Deep Learning Defences for Medical Imaging. (arXiv:2201.08661v1 [cs.CR])
Jan. 24, 2022, 2:20 a.m. | Moshe Levy, Guy Amit, Yuval Elovici, Yisroel Mirsky
cs.CR updates on arXiv.org arxiv.org
Deep learning has shown great promise in the domain of medical image
analysis. Medical professionals and healthcare providers have been adopting the
technology to speed up and enhance their work. These systems use deep neural
networks (DNN) which are vulnerable to adversarial samples; images with
imperceivable changes that can alter the model's prediction. Researchers have
proposed defences which either make a DNN more robust or detect the adversarial
samples before they do harm. However, none of these works consider an …
More from arxiv.org / cs.CR updates on arXiv.org
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