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Adaptive encryption of digital images based on lifting wavelet optimization

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Abstract

A large number of digital pictures have many security threats in the transmission on the Internet, and various encryption technologies for images have been proposed. The existing image encryption technology has a simple structure and a small key space, which cannot meet the requirement for image information security at this stage. In order to improve the effectiveness of image encryption and enhance the adaptive ability of image encryption, based on the theory of wavelet transform, chaos theory and adaptive encryption, this paper designs the overall framework of image encryption, and proposes a digital image adaptive encryption method based on lifting wavelet optimization. Firstly, the framework of image frequency domain adaptive encryption algorithm is designed, wavelet transform based on lifting is studied and analyzed, and lifting wavelet optimization method based on improved threshold method and particle swarm algorithm is given to improve the adaptability to image decomposition, so that the frequency domain coefficients obtained after wavelet decomposition can better represent the image content, thus making frequency domain encryption more targeted. Chaos mapping is used to encrypt the pixel gray value and scramble the pixel position of the decomposed low frequency coefficients of the image, thus achieving good image adaptive encryption.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Jian Wang, Wenyuan Liu & Shuai Zhang

  2. The First Hospital of Qinhuangdao, Qinhuangdao, 066000, Hebei, China

    Jian Wang

  3. Hebei Normal University of Science &Technology, Qinhuangdao, 066004, Hebei, China

    Shuai Zhang

Authors
  1. Jian Wang
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  2. Wenyuan Liu
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  3. Shuai Zhang
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Correspondence to Wenyuan Liu.

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Wang, J., Liu, W. & Zhang, S. Adaptive encryption of digital images based on lifting wavelet optimization. Multimed Tools Appl 79, 9363–9386 (2020). https://doi.org/10.1007/s11042-019-7704-3

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  • DOI: https://doi.org/10.1007/s11042-019-7704-3

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