Efficient steganalysis of images: learning is good for anticipation

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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