Block-Based Image Steganalysis
Traditional image steganalysis techniques are conducted with respect to the entire image. In this work, we aim to differentiate a stego image from its cover image based on steganalysis results of decomposed image blocks. As a natural image often consists of heterogeneous regions, its decomposition will lead to smaller image blocks, each of which is more homogeneous. We classify these image blocks into multiple classes and find a classifier for each class to decide whether a block is from a cover or stego image. Then, the steganalysis of the whole image is conducted by fusing decision results of all image blocks through a voting process. Furthermore, the performance of block-based image steganalysis in terms of block sizes and block numbers is examined. We show that a larger block size and a larger block number will result in better performance. For a given test image, there exists a trade-off between the block size and the block number. To achieve better detection accuracy, we propose to use overlapping blocks to increase the block number. Experimental results will be given to show the advantage of the proposed block-based image steganalysis approach.
Recommendation from Dr. Yun Qing Shi (NJIT, http://web.njit.edu/~shi/), who is the Editor-in-Chief of LNCS Transactions on Data Hiding and Multimedia Security (Springer), an Associate Editor of the Journal on Multidimensional Systems and Signal Processing (Springer).