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Compressive Sensing and Chaos-Based Image Compression Encryption

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Advances in Soft Computing and Machine Learning in Image Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 730))

Abstract

Compressive sensing and chaos-based image compression-encryption scheme is proposed. A two-dimensional chaotic map, the sine logistic modulation map is used to generate a chaotic sequence. The chaotic sequence is used to construct two circulant measurement matrices. The sparse representation of the plain image is obtained by employing discrete cosine transform. The transform coefficients are then measured using the two measurement matrices. Two levels of encryption are achieved. The parameters of the chaotic map acts as the key in the first level of encryption. Further, Arnold chaotic map-based scrambling is used to enhance the security of the cipher. Simulation results verify the effectiveness of the algorithm and its robustness against various attacks.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SSN College of Engineering, Chennai, India

    R. Ponuma & R. Amutha

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  1. R. Ponuma
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  2. R. Amutha
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Correspondence to R. Ponuma .

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Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. CUCEI, Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico

    Diego Alberto Oliva

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Ponuma, R., Amutha, R. (2018). Compressive Sensing and Chaos-Based Image Compression Encryption. In: Hassanien, A., Oliva, D. (eds) Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-63754-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-63754-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63753-2

  • Online ISBN: 978-3-319-63754-9

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