# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 4 Deep Learning for Medical Image Cryptography  
The use of medical images to guide diagnosis and treatment strategies, among other things, is gaining popularity as medical imaging technology becomes more widespread. For instance, images from chest CT scans and brain MRIs can be utilized to help diagnose lung disease and find brain tumors, respectively. However, because these medical images contain private and sensitive information about the patients, their disclosure could have an impact on the patients' right to privacy as well as legal repercussions for the institutions. Therefore, attempts have been made to build security solutions (such as cryptographic primitives) to secure these medical images and safeguard the privacy of the patients. The schematic of generic secure cloud-based IoMT system within the healthcare sector's heterogeneous environment is shown in Figure 5. In this type of the IoMT systems, it is expected that the medical images are transmitted to the cloud through cryptosystems in the form of encrypted data. Then, there are central cloud computing servers with deep learning models for various medical image analysis applications. These clouds return encrypted results/predictions to different healthcare centers. Readers must note that encrypted results are shown in the image form whereas it may not be necessarily true each time. These results are decrypted by authorized doctors or clinicians for various analysis purposes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Task** & **Image Modality** & **Model** & \
\begin{tabular}{c} **Attack** \\ **Scenario** \\ \end{tabular} & **Attack** &
\begin{tabular}{c} **Performance** \\ **Degradation** \\ \end{tabular} \\ \hline
[81] & 2021 & Classification & \
\begin{tabular}{c} X-ray, Fundoscopy, \\ Microscopy \\ \end{tabular} &
\begin{tabular}{c} Inception-v3 and \\ Densenet-121 \\ InceptionV3, \\ InceptionV4, \\ MobileNet, SegNet, U \\ Net and DenseNet \\ \end{tabular} & No-box & FGSM, PGD & 35\% \\
[82] & 2018 & Classification, Segmentation & \
\begin{tabular}{c} MRI, Dermoscopy \\ \end{tabular} &
\begin{tabular}{c} InceptionV3, \\ InceptionV4, \\ MobileNet, SegNet, U \\ Net and DenseNet \\ \end{tabular} & White-box, & FGSM, DeepFool, & 6-24\%/ \\
[89] & 2018 & Classification &
\begin{tabular}{c} Fundoscopy, \\ Dermoscopy, X-ray \\ \end{tabular} & ResNet50 & White-box, & PGD & 50–100\% \\
[90] & 2021 & Segmentation & X-ray & Similar to U-Net & White-box & PGD & Up to 100\% \\
[91] & 2021 & Classification & CT, X-ray & VGG-16, InceptionV3 & White-box & FGSM & Up to 90\% \\
[92] & 2020 & Detection & Chest X-ray & COVID-Net & White-box & FGSM & Up to 45\% \\  & &
\begin{tabular}{c} VGG16, VGG19, \\ Inception, \\ DenseNet169, \\ DenseNet121 \\ ResNet50 \\ \end{tabular} & White-box & UAP & Up to 72\% \\
[95] & 2020 & Segmentation &
\begin{tabular}{c} MRI scans \\ X-ray, Fundoscopy, \\ Dermoscopy \\ \end{tabular} & U-Net & White-box & FGSM, BIM, PGD, & Up to 65\% \\
[100] & 2021 & Classification &
\begin{tabular}{c} DNN \\ Dems0 CY \\ \end{tabular} & White-box & CW & Up to 100\% \\
[103] & 2018 & Classification &
\begin{tabular}{c} CNN, Hybrid \\ lesion-based model \\ \end{tabular} & No-box & I-FGSM & 45\%/0.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of attacks on tasks involving classification and segmentation using deep learning models. Cryptographic approaches are being used in various ways and reported in the literature. The following subsections cover the reported papers related to each categorized approach. Figure 6 shows the different applications of cryptographic approaches used in deep learning-based medical image processing securely. Figure 6a shows the example of encryption approach used while processing the medical images [107], whereas (b) illustrates the encrypted denoising approaches in the IoT-based healthcare systems.