Hybrid Encryption Scheme for Medical Imaging Using AutoEncoder and Advanced Encryption Standard

Abstract

Recently, medical image encryption has gained special attention due to the nature and sensitivity of medical data and the lack of effective image encryption using innovative encryption techniques. Several encryption schemes have been recommended and developed in an attempt to improve medical image encryption. The majority of these studies rely on conventional encryption techniques. However, such improvements have come with increased computational complexity and slower processing for encryption and decryption processes. Alternatively, the engagement of intelligent models such as deep learning along with encryption schemes exhibited more effective outcomes, especially when used with digital images. This paper aims to reduce and change the transferred data between interested parties and overcome the problem of building negative conclusions from encrypted medical images. In order to do so, the target was to transfer from the domain of encrypting an image to encrypting features of an image, which are extracted as float number values. Therefore, we propose a deep learning-based image encryption scheme using the autoencoder (AE) technique and the advanced encryption standard (AES). Specifically, the proposed encryption scheme is supposed to encrypt the digest of the medical image prepared by the encoder from the autoencoder model on the encryption side. On the decryption side, the analogous decoder from the auto-decoder is used after decrypting the carried data. The autoencoder was used to enhance the quality of corrupted medical images with different types of noise. In addition, we investigated the scores of structure similarity (SSIM) and mean square error (MSE) for the proposed model by applying four different types of noise: salt and pepper, speckle, Poisson, and Gaussian. It has been noticed that for all types of noise added, the decoder reduced this noise in the resulting images. Finally, the performance evaluation demonstrated that our proposed system improved the encryption/decryption overhead by 50–75% over other existing models.

Deep Learning for Medical Image Cryptography: A Comprehensive Review

“…Alslman Y. et al [122] proposed a hybrid encryption approach that encrypts the medical pictures, first using AES first and then a deep learning model (autoencoder). By using autoencoders as a feature extraction method, this hybrid model enables to have control over the size and structure of the medical images being encrypted and then communicated.…”

Optimization of Security Identification in Power Grid Data through Advanced Encryption Standard Algorithm

“…Sarita Sanap and Vijayshree More proposed an ultra-high throughput and efficient implementation of the Advanced Encryption Standard (AES) on field programmable gate arrays [1]. Alslman Yasmeen et al introduced a hybrid encryption scheme for medical imaging by combining AutoEncoder and AES [2]. Sabreen Ali Hussein et al evaluated image encryption algorithms, including the Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and AES techniques [3].…”