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Optimal detection of outguess using an accurate model of DCT coefficients

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Abstract

This paper presents an optimal statistical test for the detection of OutGuess steganographic algorithm using an accurate statistical model of Discrete Cosine Transform (DCT) coefficients. First, this paper presents the proposed novel statistical model of quantized DCT coefficients. Then, this model is applied to design an optimal statistical test for the detection of OutGuess data hiding scheme. To this end, the detection of hidden data is cast within the framework of hypothesis testing theory. The optimal Likelihood Ratio Test (LRT) is first presented. Then, for a practical application, a Generalized LRT is proposed using Maximum Likelihood Estimations of unknown parameters. Large scale numerical results show that the proposed approach allows the reliable and efficient detection of OutGuess.
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Dates and versions

hal-02356425 , version 1 (08-11-2019)

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Thanh Hai Thai, Rémi Cogranne, Florent Retraint. Optimal detection of outguess using an accurate model of DCT coefficients. 2014 IEEE International Workshop on Information Forensics and Security (WIFS), Dec 2014, Atlanta, United States. pp.179-184, ⟨10.1109/WIFS.2014.7084324⟩. ⟨hal-02356425⟩
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