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On Chaos based duo confusion duo diffusion for colour images

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Abstract

In recent years, owing to the frequent flow of digital images worldwide over the transmission media, it has become essential to secure them from leakages. Moreover, many applications like military image databases, confidential video conferencing, medical imaging systems, cable TV, online personal photograph albums, etc., require reliable, fast and robust security systems to transmit digital images. Among the available encryption schemes, chaos-based (Chaos means randomness) encryption techniques are considered acceptable, which provides randomness and high security. In this work, chaos assisted Color image encryption has been proposed. Initially, the Colour image is split into its RGB planes. To accomplish the encryption on RGB planes, duo confusion and duo diffusion through chaotic maps and attractors have been performed. Confusion and diffusion have been carried out in each plane in two stages, namely – block and plane, for which Logistic Map, Lorenz Attractor, Tent map and Lu attractor with different initial conditions and seeds are used. Finally, the separated RGB planes have been merged to produce an encrypted image. Standard encryption analyses such as – statistical attack analyses, encryption quality analyses, keyspace analyses, and chi-square tests have been performed to evaluate the proposed work. To evidence, the proposed method’s attack resistance capability, chosen plain text, noise and cropping attacks have been conducted

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Acknowledgements

Authors thank the Department of Science & Technology, New Delhi, for the FIST funding (SR/FST/ET-I/2018/221(C)). Also, the authors wish to thank the Intrusion Detection Lab at School of Electrical & Electronics Engineering, SASTRA Deemed University, for providing infrastructural support to carry out this research work.

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Correspondence to Amirtharajan Rengarajan.

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Sridevi, A., Sivaraman, R., Balasubramaniam, V. et al. On Chaos based duo confusion duo diffusion for colour images. Multimed Tools Appl 81, 16987–17014 (2022). https://doi.org/10.1007/s11042-022-12471-5

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