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Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps

Rehman, Mujeeb Ur ORCID: https://orcid.org/0000-0002-4228-385X, Shafique, Arslan ORCID: https://orcid.org/0000-0001-7495-2248, Khalid, Sohail ORCID: https://orcid.org/0000-0003-3907-0236 and Hussain, Iqtadar (2021) Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps. IEEE Access, 9. pp. 52277-52291.

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Abstract

The advancement in wireless communication has encouraged the process of data transferring through the Internet. The process of data sharing via the Internet is prone to several attacks. The sensitive information can be protected from hackers with the help of a process called Encryption. Owing to the increase in cyber-attacks, encryption has become a vital component of modern-day communication. In this article, an image encryption algorithm is suggested using dynamic substitution and chaotic systems. The suggested scheme is based upon the chaotic logistic map, chaotic sine maps and the dynamical substitution boxes (S-boxes). In the proposed scheme, the S-box selection is according to the generated sequence by deploying the chaotic sine map. To evaluate the robustness and security of the proposed encryption scheme, different security analysis like correlation analysis, information entropy, energy, histogram investigation, and mean square error are performed. The keyspace and entropy values of the enciphered images generated through the proposed encryption scheme are over 2 278 and 7.99 respectively. Moreover, the correlation values are closer to zero after comparison with the other existing schemes. The unified average change intensity (UACI) and the number of pixel change rate (NPCR) for the suggested scheme are greater than 33, 99.50% respectively. The simulation outcomes and the balancing with state-of-the-art algorithms justify the security and efficiency of the suggested scheme

Item Type: Article
Status: Published
DOI: https://doi.org/10.1109/ACCESS.2021.3069591
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/8167

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