This paper investigates the statistical detection of JSteg steganography. The approach is based on a statistical model\nof discrete cosine transformation (DCT) coefficients challenging the usual assumption that among a subband all the\ncoefficients are independent and identically distributed (i. i. d. ). The hidden information-detection problem is cast in\nthe framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the\nlikelihood ratio test (LRT) is presented, and its performances are theoretically established. The statistical performance\nof LRT serves as an upper bound for the detection power. For a practical use where the distribution parameters are\nunknown, by exploring a DCT channel selection, a detector based on estimation of those parameters is designed. The\nloss of power of the proposed detector compared with the optimal LRT is small, which shows the relevance of the\nproposed approach.
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