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An algorithm for embedding information in digital images based on discrete wavelet transform and learning automata

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Abstract

The paper presents a new algorithm for embedding information in the frequency domain of the discrete wavelet-transform (DWT) of digital images. A block version of quantization index modulation (QIM) is used as a basic embedding operation. A distinctive feature of the algorithm consists in the adaptive selection of the data block size depending on the local properties of the cover image. It has been shown experimentally that for image areas containing a larger number of edge pixels, it is necessary to select blocks of greater length in the corresponding frequency domains. In addition, the problem of optimization the distortions in the blocks of DWT coefficients is substantiated and solved in order to improve the quality of embedding. A computing model of learning automata is used to solve this problem. The advantage of the obtained algorithm is that the receiver of the stego-image does not need additional information to extract the embedded message. The algorithm is highly efficient in terms of the main criteria of embedding quality and can be used both for embedding digital watermarks and arbitrary messages.

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Acknowledgments

The study was supported by the grant of the Russian Science Foundation (project No 19-71-00106). We are very grateful to the anonymous referees for their constructive comments and helpful suggestions to improve the quality of this paper.

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Authors and Affiliations

  1. HSE University, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russia

    Oleg Evsutin

  2. V. A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya street, Moscow, 117997, Russia

    Oleg Evsutin

  3. Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, Tomsk, 634050, Russia

    Pavel Kultaev

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  1. Oleg Evsutin
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  2. Pavel Kultaev
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Correspondence to Oleg Evsutin.

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Evsutin, O., Kultaev, P. An algorithm for embedding information in digital images based on discrete wavelet transform and learning automata. Multimed Tools Appl 80, 11217–11238 (2021). https://doi.org/10.1007/s11042-020-10316-7

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  • DOI: https://doi.org/10.1007/s11042-020-10316-7

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