Abstract
This paper introduces an approach to estimate energy of pixel associated with its neighbors. We define an energy function of a pixel which replaces the pixel value by mean or median value of its neighborhood. The correlations inherent in a cover signal can be used for steganalysis, i.e, detection of presence of hidden data. Because of the interpixel dependencies exhibited by natural images this function was able to differentiate between cover and stego image. Energy function was modeled using Gibbs distribution even though pixels in an image have the property of Markov Random Field. Our method is trained to specific embedding techniques and has been tested on different textured images and is shown to provide satisfactory result in classifying cover and stego using energy distribution.
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References
Bouman, C.: Model based image processing. Purdue University (2013)
Chellappa, R., Jain, A.: Markov random fields. theory and application, vol. 1 (1993)
Fridrich, J., Goljan, M.: Practical steganalysis of digital images-state of the art. In: Proceedings of SPIE, vol. 4675, pp. 1–13
Goljan, M., Fridrich, J., Cogranne, R.: Rich model for steganalysis of color images. In: IEEE Workshop on Information Forensic and Security, Atlanta, GA (2014)
Johnson, N.F., Jajodia, S.: Exploring steganography: seeing the unseen, vol. 31, pp. 26–34. IEEE (1998)
Köster, U., Lindgren, J.T., Hyvärinen, A.: Estimating markov random field potentials for natural images. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds.) ICA 2009. LNCS, vol. 5441, pp. 515–522. Springer, Heidelberg (2009)
Krizhevsky, A., Hinton, G.E., et al.: Factored 3-way restricted boltzmann machines for modeling natural images. In: International Conference on Artificial Intelligence and Statistics, pp. 621–628 (2010)
Li, S.Z.: Markov random field modeling in computer vision. Springer-Verlag New York, Inc. (1995)
Osindero, S., Hinton, G.E.: Modeling image patches with a directed hierarchy of markov random fields. In: Advances in Neural Information Processing Systems, pp. 1121–1128 (2008)
Rangarajan, A., Chellappa, R.: Markov random field models in image processing. Citeseer (1995)
Ranzato, M., Hinton, G.E.: Modeling pixel means and covariances using factorized third-order boltzmann machines. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2551–2558. IEEE (2010)
Ranzato, M., Mnih, V., Susskind, J.M., Hinton, G.E.: Modeling natural images using gated mrfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 9, 2206–2222 (2013)
Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 860–867. IEEE (2005)
Wang, C., Komodakis, N., Paragios, N.: Markov random field modeling, inference and learning in computer vision and image understanding: A survey. Computer Vision and Image Understanding 117(11), 1610–1627 (2013)
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Amritha, P.P., Sreedivya Muraleedharan, M., Rajeev, K., Sethumadhavan, M. (2016). Steganalysis of LSB Using Energy Function. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_48
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DOI: https://doi.org/10.1007/978-3-319-23036-8_48
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