Image Encryption and Steganography Using a Memristive-Coupled Neural Network Hyperchaotic System for the Healthcare Industry

This paper analyzes integral aspects of digital rights management and encryption that ensure the privacy of digital content to their owners. This is crucial for institutions such as the healthcare sector, where medical images such as X-rays and CT scans, as well as patient information, should not be disclosed to unauthorized users. However, there exists a challenge of choosing a hyperchaotic system of differential equations to carry out image encryption that generates results superior to those in the literature. This paper presents an approach to address this issue, and a solution is developed for encrypting images using a memristive hyperchaotic neural network as well as a steganography layer to further enhance security. Well-known performance evaluation metrics in the field are used in order to analyze the effectiveness of the proposed method and to highlight its value in protecting critical medical information. The implementation is carried out using Wolfram Mathematica® due to its powerful commands and visualization tools. Furthermore, the steps of implementing the proposed scheme start with dividing the 3D brain image into 2D slices and converting it into a bit-stream. Patient information is embedded using Least Significant Bit (LSB) embedding, and an encryption key is generated from the memristive neural network hyperchaotic system and is XORed with the resulting image bits after embedding. The image is then transformed using a Jigsaw transform and an S-box generated from a 6D hyperchaotic system is applied for added security. The proposed method proves to be superior to the results in the literature, having a key space of 2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2657</sup>, an NPCR of 99.654 and a UACI of 45.1931, which show that the method is effective and has enhanced security to protect confidential medical information such as patient information, X-rays and CT scans.