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Cellular Neural Network-Based Medical Image Encryption

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Abstract

This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes.

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Authors and Affiliations

  1. Siddaganga Institute of Technology, Visvesvaraya Technological University, Tumakuru, Karnataka, 572103, India

    S. J. Sheela, K. V. Suresh & A. Sanjay

  2. Corporate Research India, ABB, Bangalore, Karnataka, 560048, India

    Deepaknath Tandur

Authors
  1. S. J. Sheela
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  2. K. V. Suresh
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  3. Deepaknath Tandur
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  4. A. Sanjay
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Correspondence to S. J. Sheela.

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Sheela, S.J., Suresh, K.V., Tandur, D. et al. Cellular Neural Network-Based Medical Image Encryption. SN COMPUT. SCI. 1, 346 (2020). https://doi.org/10.1007/s42979-020-00371-0

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  • DOI: https://doi.org/10.1007/s42979-020-00371-0

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