Abstract
This paper presents an efficient image encryption scheme based on permutation followed by diffusion, where both of these phases use 2-d Sine logistic modulation map (SLMM) with different initial values. In addition, diffusion uses another map as 1-d Logistic chaotic map (LCM). The initial values of these chaotic maps are obtained from an external key of 64 bytes along with 32-byte hash value from the corresponding plain-image to incorporate plain-text sensitivity. Initially, confusion of the plain-image is implemented by applying row-level and column-level permutations. Then, this permuted image is used for subsequent diffusion, applied on block-level considering block size of 64 bytes. This diffusion process is accomplished by overlaying with chaotic matrix derived from LCM, followed by substitution of those overlaid bytes by DNA encoding along with SLMM to attain an encrypted image with an entropy nearly 8. Furthermore, all the chaotic values generated from the aforementioned maps are highly sensitive on the key as well as on the plain-image. This scheme is thoroughly verified on different sized plain-images with modern statistical analyses to prove the robustness of this scheme. Eventually, comparison with other schemes reinforces its competence and suitability to implement it in real-time system.
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Acknowledgements
We are thankful to the Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Serampore, Hooghly, West Bengal, India and MCKV Institute of Engineering, Howrah, West Bengal, India for giving us the platform for planning and developing this work using all departmental facilities. We are also deeply grateful to the editors for smooth and fast handling of the manuscript. We would also like to thank the anonymous reviewers for their valuable advices and suggestions to improve the quality of this paper.
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Naskar, P.K., Bhattacharyya, S., Mahatab, K.C. et al. An efficient block-level image encryption scheme based on multi-chaotic maps with DNA encoding. Nonlinear Dyn 105, 3673–3698 (2021). https://doi.org/10.1007/s11071-021-06761-0
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DOI: https://doi.org/10.1007/s11071-021-06761-0