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Efficient steganalysis of images: learning is good for anticipation

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Abstract

This paper describes a Bayesian formalism for digital image steganalysis allowing the detection of stego images, the identification of the steganographic algorithm used, the estimation of message length and location, and anticipation in the case of embedding using an unknown steganographic algorithm. A Bayesian multinomial logistic regression based on a variational approximation is proposed. Detection, identification, and anticipation involve discriminative learning in feature space. Estimation requires the fusion of classifiers allowing discrimination between fully embedded and cover subimages of different sizes. The validation on JPEG images shows that the proposed scheme is effective and allows the anticipation of unknown steganographic algorithms.

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Acknowledgments

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC) and Bell Canada. This work is dedicated to the memory of the late Zahir Menadi who helped in the early stages.

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Authors and Affiliations

  1. Département d’informatique, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada

    Djemel Ziou & Reza Jafari

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  1. Djemel Ziou
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  2. Reza Jafari
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Correspondence to Djemel Ziou.

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Ziou, D., Jafari, R. Efficient steganalysis of images: learning is good for anticipation. Pattern Anal Applic 17, 279–289 (2014). https://doi.org/10.1007/s10044-012-0303-9

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