Leila Omrani - Master Student of Software Engineering, Islamic Azad University of Qazvin, Iran
Karim Faez - Electrical Engineering Department, University of Amirkabir, Tehran, Iran

Feature extraction is the base of steganalysis which is a part of image processing research field. This article has proposed a steganalysis method for digital images. Commonsteganalysis techniques go over the entire image; this will reduce their focus on higher frequencies in which there is a higherprobability for hidden messages. Accordingly, in this article, images are first decomposed into smaller blocks and then optimalwavelet packet decomposition method is applied to extract thefeatures of each block. In the proposed algorithm, characteristic function moments obtained from wavelet sub-bands are used asfeatures. These features are arranged in a tree structure and then an entropy cost function is used to select the optimal values ofthese features. In the next step, the blocks are classified in several categories and a classifier appropriate to the features of eachcategory is applied to distinguish cover or stego blocks. Finally, the majority vote rule is applied on the results obtained from theblocks to determine whether the entire image is a cover or stegoimage. The experimental results of this steganalysis method show its high accuracy as compared to the common steganalysis algorithms in the frequency domain.

Steganalysis, Steganography, Wavelet Packet Decomposition, Blocking