Cellular Neural Network-Based Medical Image Encryption

Abstract

This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes.

A New V-Net Convolutional Neural Network Based on Four-Dimensional Hyperchaotic System for Medical Image Encryption

“…where p(R i ) is the occurrence frequency of pixel value i in the ciphertext image R. Table 2 is the comparison of global information entropy with other methods including FRFT [29], ASFS [30], and CENN [31]. However, there are some deficiencies in the global information entropy and the measurement of the image before and after encryption is not accurate.…”