Future Computing and Informatics Journal
Abstract
Researchers are motivated to use artificial intelligence in biometrics, medical imaging encryption, as well as cybersecurity due to its rapid progress. An encryption method for CT scans—which are used to diagnose COVID-19 disease—is proposed in this study. The suggested encryption method creates a connection among an individual's face picture and CT image to increase confidentiality. The simple CT picture is first enhanced with a host image. An encryption key is multiplied by the final result. This key is produced by applying a Convolutional Neural Network (CNN) to recognize characteristics from people's face photographs. Additionally, a straightforward CNN with three convolutional groups is suggested. "Rectified Linear Unit (ReLU)", "batch normalization layer", and "convolutional layer" make up each group. Finally, Discrete Wavelet Transform (DWT) is applied on the product of deep facial features and the image resulted from adding the CT image and the host image. Decryption is performed in the reverse order to obtain the original CT image. Furthermore, we examine and assess the effectiveness of the suggested method against three distinct forms of attacks: rotation, Speckle noise, and Salt & Pepper noise. Three scenarios are covered in the studies where decryption is carried out: using the right key, using the right key with a little modification, and using the incorrect key. Metrics including MSE, PSNR, Entropy, CC, histogram analysis, and elapsed time are used to gauge how well the suggested method performs. A maximum of 0.4361 seconds have elapsed.
Recommended Citation
Abdelgwad, Mohamed Attia; Abed, Amira Hassan; and bahloul, Mahmoud
(2023)
"Authenticated Diagnosing of COVID-19 using Deep Learning-based CT Image Encryption Approach,"
Future Computing and Informatics Journal: Vol. 8:
Iss.
2, Article 4.
Available at:
https://digitalcommons.aaru.edu.jo/fcij/vol8/iss2/4
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