Journal of Information Security and Applications, Volume (68), Year (2022-8) , Pages (103254-103265)

Title : ( Non-additive image steganographic framework based on variational inference in Markov Random Fields )

Authors: Behnaz Abdollahi , Ahad Harati , Amir Hossein Taherinia ,

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The majority of image steganography methods lie within the additive scheme that assumes the pixel modifications are independent. Whereas, adapting the embedding modifications based on the mutual embedding impact of neighboring pixels reduce changes in the higher-order statistics of the cover imposed by embedding. However, non-additive schemes are more challenging due to the lack of practical embedding codes that minimize an arbitrary distortion function. In this paper, we propose a general non-additive image steganographic framework based on Markov Random Fields (MRF) which generalizes a few existing research in many aspects and offers an elegant way to model the mutual dependencies between neighborhood modifications in terms of pairwise cliques. The proposed model satisfies spatial coherence by penalizing the desynchronized embedding changes, which orients the changes towards the direction of the majority of changes in the neighbors. Mean Field (MF) inference is used to iteratively estimate the marginal probability at each pixel based on its neighbors so that the original interactions are information projected to the resulting marginals. MF replaces the values of variables with expectations so the result is compatible with practical embedding methods. The proposed framework can be applied to any additive scheme; the initial cost assignment is done by an additive method and MRF modeling defines a non-additive distortion related to statistical detectability that encourages the adjacent changes to synchronize. We study our framework in both symmetric and asymmetric schemes. In the symmetric scheme, a parallel MF is used as an adaptive filter to encourage synchronized embedding changes. The framework can upgrade to an asymmetric scheme that reduces the detectability by conditioning embedding based on the message using clamping exerted in a structural MF. Therefore, our framework is compatible with and generalizes existing non-additive methods, and as shown in experimental results, outperforms the state-of-the-art in both schemes.


, Steganography, Non-additive distortion, Markov Random Field, Variational inference, Mean field, Asymmetric embedding
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author = {Abdollahi, Behnaz and Harati, Ahad and Taherinia, Amir Hossein},
title = {Non-additive image steganographic framework based on variational inference in Markov Random Fields},
journal = {Journal of Information Security and Applications},
year = {2022},
volume = {68},
month = {August},
issn = {2214-2126},
pages = {103254--103265},
numpages = {11},
keywords = {Steganography; Non-additive distortion; Markov Random Field; Variational inference; Mean field; Asymmetric embedding},


%0 Journal Article
%T Non-additive image steganographic framework based on variational inference in Markov Random Fields
%A Abdollahi, Behnaz
%A Harati, Ahad
%A Taherinia, Amir Hossein
%J Journal of Information Security and Applications
%@ 2214-2126
%D 2022