This paper introduces a novel method to detect the typical LSB (Least Significant Bit) embedding and the LSB matching steganography methods applied to grayscale images. The proposed method determines the changes made to some selected features extracted from the gray level run length matrix. It is shown that the run length characteristics can significantly be affected by the embedded message bits, so can be employed as a measure that is quite sensitive to the arrangements of the image pixel values. The extracted features are examined by a nonlinear SVM (Support Vector Machine) classifier with quadratic kernel that can distinguish between stego and clean images. Experimental results are given to demonstrate the competitively higher performance of the proposed method, as compared to other well-known steganalysis methods, at different embedding rates.
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