Abstract
Steganography is one of the methods used for the hidden exchange of information and it can be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. In this way, if successfully it is achieved, the message does not attract attention from eavesdroppers and attackers. Using steganography, information can be hidden in different embedding mediums, known as carriers. These carriers can be images, audio files, video files, and text files. The focus in this paper is on the use of an image file as a carrier. The proposed approach is based on backpropagation neural networks. The essential part of this article aims to verify the proposed approach in an experimental study. Further, contemporary method of application and results are presented in this paper as an example.
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Blum, L., Blum, M., Shub, M.A.: Simple unpredictable pseudo-random number generator. SIAM J. Comput. 15(2), 364–383 (1986)
Dasgupta, K., Mandal, J.K., Dutta, P.: Hash based least significant bit technique for video steganography (HLSB). Int. J. Secur. Priv. Trust Manage. 2(2) (2012)
Holoska, J., Oplatkova, Z., Zelinka, I., Senkerik, R.: Comparison between neural network steganalysis and linear classification method stegdetect. In: 2010 Second International Conference on Computational Intelligence, Modeling and Simulation (CIMSiM), vol. 10, pp. 15–20 (2010)
Jarušek, R., Volná, E., Kotyrba, M.: Steganography based on neural networks (a preliminary study). In: Proceedings of the 20th International Conference on Soft Computing, Mendel 2014, Brno, Czech Republic, pp. 223–228 (2014)
Liu, S., Yao, H., Gao, W.: Steganalysis based on wavelet texture analysis and neural network. In: Fifth World Congress on Intelligent Control and Automation, 2004. WCICA 2004, vol. 5, pp. 4066–4069 (2004)
Mohammadi, F.G., Abadeh, M.S.: A survey of data mining techniques for steganalysis. Recent Advances in Steganography, pp. 1–25 (2012)
Nissar, A., Mir, A.H.: Classification of steganalysis techniques study. Digit. Signal Process. 20(6), 1758–1770 (2010)
Schatzman, J.C.: Accuracy of the discrete Fourier transform and the fast Fourier transform. SIAM J. Sci. Comput. 17, 1150–1166 (1996). doi:10.1137/s1064827593247023
Seung, S.: Multilayer perceptrons and backpropagation learning. 9.641 Lecture 4. 1–6. http://hebb.mit.edu/courses/9.641/2002/lectures/lecture04.pdf (2002)
Shaohui, L., Hongxun, Y., Wen, G.: Neural network based steganalysis in still images. In: Proceedings of the 2003 International Conference on Multimedia and Expo, ICME’03, vol. 20, pp. 509–512 (2003)
Vani, G.B., Prasad, E.V.: Scalable and highly secured image steganography based on Hopfield chaotic neural network and wavelet transforms. Int. J. Comput. Sci. Issues 10(3), 1 (2013)
Starha, P., Martisek, D., Matousek, R.: Numerical methods of object reconstruction using the method of moments. In: Proceedings of 20th International Conference on Soft Computing—Mendel 2014. Mendel series vol. 2014, pp. 241–248, Brno, ISSN: 1803-3814 (2014)
Acknowledgments
The research described here has been financially supported by University of Ostrava grant SGS17/PřF/2015. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the sponsors.
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Jarušek, R., Volna, E., Kotyrba, M. (2015). Neural Network Approach to Image Steganography Techniques. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_26
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DOI: https://doi.org/10.1007/978-3-319-19824-8_26
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