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Neural network functional models and algorithms for information conversion in order to create digital watermarks

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Abstract

There are considered functional neural network models and algorithms of information conversion that providing steganographic encoding of messages in the form of digital watermarks (DWM) into arbitrary objects—containers (digital images) and their subsequent decoding with minimal container distortion. The approach is based on theoretical justification of creating hetero- and autoassociative contraction mappings of the container fragments using direct propagation neural networks. The dependencies of the DWM quality indicators describing the container distortion level, as well as the probability of error at the DWM binary sequence decoding were obtained for model images in the form of random fields, as well as for real images.

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Authors and Affiliations

  1. Voronezh State University, Voronezh, Russia

    A. A. Sirota, M. A. Dryuchenko & E. Yu. Mitrofanova

Authors
  1. A. A. Sirota
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  2. M. A. Dryuchenko
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  3. E. Yu. Mitrofanova
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Corresponding author

Correspondence to A. A. Sirota.

Additional information

Original Russian Text © A.A. Sirota, M.A. Dryuchenko, E.Yu. Mitrofanova, 2015, published in Izv. Vyssh. Uchebn. Zaved., Radioelektron., 2015, Vol. 58, No. 1, pp. 3–16.

ORCID: 0000-0002-5785-8513

This research is supported by Russian Foundation for Basic Research. Grant No. 13-01-97507.

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Sirota, A.A., Dryuchenko, M.A. & Mitrofanova, E.Y. Neural network functional models and algorithms for information conversion in order to create digital watermarks. Radioelectron.Commun.Syst. 58, 1–10 (2015). https://doi.org/10.3103/S073527271501001X

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  • DOI: https://doi.org/10.3103/S073527271501001X

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