JPEG Steganalysis Using Estimated Image and Markov Model

This paper proposed a JPEG steganalysis scheme based on Markov model using features derived from the detected image and the estimated image. Estimated image is created by cropping four pixels of the detected image from left line. And the estimated image is similar with the original image with the statistical characteristics. From both of the detected image and estimated image, Markov process is applied to modeling the difference JPEG 2-D arrays along horizontal, vertical, and diagonal directions so as to utilize the high order statistics for enhancing changes caused by JPEG steganography. Support vector machines (SVM) are utilized as classifier. The experimental results have proved that the proposed method is effective in attacking by the existing steganalyzers.

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