A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations

Abstract

Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.

A Multi-Image Encryption Based on Sinusoidal Coding Frequency Multiplexing and Deep Learning

“…In the case of image feature leakage, it is vital that the encryption scheme can resis statistical analysis. The histogram of the image shows the distribution of pixel values in the image, so the histogram is a key indicator reflecting the robustness of the image en cryption scheme [38]. The histograms of four plaintext images, ciphertext images, and de crypted images are shown in Figure 14.…”

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing

“…Image encryption methods are currently available using various techniques, such as chaotic systems [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], DNA coding [ 18 , 19 , 20 , 21 ], substitution boxes [ 22 , 23 , 24 ], and cellular automata [ 25 , 26 , 27 , 28 ]. Chaotic phenomena [ 29 , 30 ] are seemingly random irregular motions that occur in a deterministic system—a system described by a deterministic theory that behaves in an uncertain, irreducible, and unpredictable manner.…”

“…One-dimensional chaotic systems have advantages, such as simple structures, easy implementation, and low computational complexity [ 11 ], but they also have disadvantages, such as small key spaces and low security [ 8 , 9 ]. To solve this problem, some MD chaotic systems have been applied to image encryption [ 12 , 13 , 14 , 15 , 16 , 17 ]. The encryption algorithms based on MD systems have larger key spaces and higher security levels; however, MD chaotic systems are relatively difficult to implement in hardware or software and the respective encryption systems have high computational complexity [ 11 ].…”

Designing 1D Chaotic Maps for Fast Chaotic Image Encryption

“…In the image encryption system that is based on chaotic maps, the encryption system includes a pair of linear (i.e., permutation) -non-linear (i.e., diffusion) conversions. Some of the encryption systems are repeating this procedure to raise the encryption strength [21][22][23][24][25]. However, in those algorithms, a large number of iterations of chaotic maps is required to produce large sequences to be utilised in permutation and diffusion steps.…”