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Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT - 2015


Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT - 2015


This paper introduces a novel feature set for steganalysis of JPEG pictures. The features are built as 1st-order statistics of quantized noise residuals obtained from the decompressed JPEG image using sixty four kernels of the discrete cosine remodel (DCT) (the thus-referred to as undecimated DCT). This approach will be interpreted as a projection model in the JPEG domain, forming so a counterpart to the projection spatial wealthy model. The most appealing facet of this proposed steganalysis feature set is its low computational complexity, lower dimensionality as compared with different wealthy models, and a competitive performance with respect to previously proposed JPEG domain steganalysis features.

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