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Statistical detection of Jsteg steganography using hypothesis testing theory

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Abstract

This paper investigates the statistical detection of Jsteg steganography. The approach is based on the statistical model of Discrete Cosine Transformation (DCT) coefficients. The hidden information detection problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test (LRT) is presented and its performances are theoretically established. The statistical performance of LRT serves as an upper bound of the detection power. For a practical use, when the distribution parameters are unknown, a detector based on estimation of those parameters is designed. The loss of power of the proposed detector, compared with the optimal LRT is small, which shows the relevance of the proposed approach.
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Dates and versions

hal-02362374 , version 1 (13-11-2019)

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Tong Qiao, Cathel Zitzmann, Florent Retraint, Rémi Cogranne. Statistical detection of Jsteg steganography using hypothesis testing theory. 2014 IEEE International Conference on Image Processing (ICIP), Oct 2014, Paris, France. pp.5517-5521, ⟨10.1109/ICIP.2014.7026116⟩. ⟨hal-02362374⟩
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