Author: A. A. Sirota, M. A. Dryuchenko, E. Yu. Mitrofanova (Voronezh State University, Voronezh, Russia)
Radioelectronics and Communications Systems, January 2015, Volume 58, Issue 1, pp 1–10 (03 February 2015)
DOI: https://doi.org/10.3103/S073527271501001X
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.
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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