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

Shafique, Arslan ORCID: https://orcid.org/0000-0001-7495-2248, Ahmed, Jameel, Rehman, Mujeeb Ur ORCID: https://orcid.org/0000-0002-4228-385X and Hazzazi, Mohammad Mazyad ORCID: https://orcid.org/0000-0002-7945-9994 (2021) Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain. IEEE Access, 9. pp. 59108-59130.

Noise-Resistant_Image_Encryption_Scheme_for_Medical_Images_in_the_Chaos_and_Wavelet_Domain.pdf - Published Version
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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.

Item Type: Article
Status: Published
DOI: https://doi.org/10.1109/ACCESS.2021.3071535
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/8168

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