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A novel approach to encrypt multiple images using multiple chaotic maps and chaotic discrete fractional random transform

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Abstract

Due to recent advances in network technology, it has become easier to send multiple images together. Hence, image encryption using multiple images is an emerging area of interest amidst researchers. The paper proposes a multiple-image encryption technique based on the chaotic confusion-diffusion strategy and chaotic Discrete Fractional Random Transform (DFRNT). Initially, odd and even bits of three input images (M × N) are separated and a composite image (3M × 2N) is derived by combining the even and odd bits of every image. Secondly, chaotic confusion-diffusion strategy using four chaotic maps Arnold Cat Map (ACM), Logistic-Tent Map (LTM), Logistic-Sine Map (LSM) and Tent-Sine Map (TSM) is applied to scramble and diffuse the pixel values of the composite image. Thereafter, three diffused images are extracted from the diffused composite image. Finally, chaotic DFRNT uses Tent-Sine Map (TSM) to generate the random kernel transform matrix for itself and encrypt the extracted images into a single image. The parameters Histogram, Correlation distribution (CD), Correlation Coefficient (CC), Entropy, Number of Pixel Change Rate (NPCR) and Unified Averaged Changed Intensity (UACI) have been computed to show the potential of the proposed encryption technique.

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Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith Banasthali, Newai, India

    Ankita Bisht

  2. Department of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, India

    Mohit Dua

  3. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, Haryana, India

    Shelza Dua

Authors
  1. Ankita Bisht
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  2. Mohit Dua
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  3. Shelza Dua
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Correspondence to Ankita Bisht.

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Bisht, A., Dua, M. & Dua, S. A novel approach to encrypt multiple images using multiple chaotic maps and chaotic discrete fractional random transform. J Ambient Intell Human Comput 10, 3519–3531 (2019). https://doi.org/10.1007/s12652-018-1072-0

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  • DOI: https://doi.org/10.1007/s12652-018-1072-0

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