Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain

Abstract

In this paper, a noise-resistant image encryption scheme is proposed. We have used a cubic-logistic map, Discrete Wavelet Transform (DWT), and bit-plane extraction method to encrypt the medical images at the bit-level rather than pixel-level. The proposed work is divided into three sections; In the first and the last section, the image is encrypted in the spatial domain. While the middle section of the proposed algorithm is devoted to the frequency domain encryption in which DWT is incorporated. As the frequency domain encryption section is a sandwich between the two spatial domain encryption sections, we called it a "sandwich encryption." The proposed algorithm is lossless because it can decrypt the exact pixel values of an image. Along with this, we have also gauge the proposed scheme's performance using statistical analysis such as entropy, correlation, and contrast. The entropy values of the cipher images generated from the proposed encryption scheme are more remarkable than 7.99, while correlation values are very close to zero. Furthermore, the number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed encryption scheme is higher than 99.4% and 33, respectively. We have also tested the proposed algorithm by performing attacks such as cropping and noise attacks on enciphered images, and we found that the proposed algorithm can decrypt the plaintext image with little loss of information, but the content of the original image is visible.INDEX TERMS Discrete Wavelet Transform (DWT), Chaotic map, Medical images, Bit-plane decomposition, Security analysis of medical images.

A Novel Machine Learning Technique for Selecting Suitable Image Encryption Algorithms for IoT Applications

“…Image Encryption in Spatial Domain. Spatial domain encryption has advanced significantly since incorporating chaos theory to secure digital images [18]. In the past few decades, chaos has been widely used in image encryption due to its several tremendous properties, such as sensitivity to initial conditions, nonperiodicity, and ability to generate pseudorandom numbers.…”

“…It is not always required to use a forward operation of any frequency transform such as DWT; one can also use its reverse operation Inverse Discrete Wavelet Transform (IDWT) to secure the digital images [43]. In [18], Shafique et al proposed a DWT-based cryptosystem in which chaos and bit-plane extraction are the major parts. The whole scheme is consisting of three sections; the first and last sections are dependent on the spatial domain encryption while the middle section is devoted to the frequency domain section.…”

“…As a lot of mathematical operations are included in the proposed scheme, it is somehow slower than the other existing schemes [13,[44][45][46]. Therefore, the scheme proposed in [18] is not suitable for real-time applications. The image encryption schemes presented in this section are based on chaos theory, bit-plane extraction, frequency transformation, and spatial domain transformation.…”

“…Time-efficient encryption techniques are always required for real-time applications. Various forms of pixel transformations may be employed in image encryption, including permutation, substitution, the Discrete Wavelet Transform (DWT) [18], the Discrete Cosine Transform (DCT) [19], and the Discrete Fourier Transform (DFT) [20]. All of these approaches have been extensively utilised over the last several decades and proposed a variety of algorithms, some of which are resistant to various types of security attacks, including ciphertext-only attacks, brute force attacks, and plaintext-only attacks.…”

Securing E-Healthcare Images Using an Efficient Image Encryption Model

“…Logistic map was applied to obtain the secret keys and perform the substitution operation. In [38], Shafique et al proposed a medical image encryption scheme using DWT, logistic map, and bit-plane extraction system. In [39], Ravichandran et al protected the medical images by using chaotic maps and deoxyribonucleic acid (DNA).…”