A new image steganalysis method using block based optimal wavelet packet decomposition

Abstract

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