| 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. We also target at the design of a multi-classifier which classifies stego images depending on their steganographic algorithms. 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. Consequently, the steganalysis of the whole image can be conducted by fusing steganalysis results of all image blocks through a decision fusion process. Experimental results will be given to show the advantage of the proposed block-based image steganalysis for both binary classifier and multi-classifier.;In addition, performance study on block-based image steganalysis in terms of block sizes and block numbers will be given in this work. First, we analyze the dependence of the steganalysis performance on one of these two factors, and show that a larger block size and a larger block number will lead to better steganalysis performance. Our study is verified by experimental results. For a given test image, there exists a trade-off between the block size and the block number. To exploit both effectively, we propose to use overlapping blocks to improve the steganalysis performance furthermore. Moreover, additional performance improvement of block-based image steganalysis with different number of classes and different classifiers will be shown with experimental results.;Decision fusion for block-based image steganalysis will be discussed. As multiple block decisions are obtained from each image, decision fusion will play a crucial role in combining all the block decisions together to make a final decision for a given image. Among decision fusion techniques at different levels with different topologies, decision level fusion with parallel topology will be used for block-based image steganalysis. In addition, the importance of block decision result will be considered for decision fusion to improve the steganalysis performance. Experimental results with different decision level fusion techniques for block-based image steganalysis will be presented.;Content-dependent feature selection for block-based image steganalysis will be proposed to reduce computational complexity with a significantly smaller number of features. Depending on the characteristic of block type, features with high discriminatory power will be selected for each block type. Several approaches to measure feature discriminatory power will be introduced. Finally, experimental result, which shows performance improvement using content-dependent feature selection, will be presented. |
