Block-based image steganalysis: Algorithm and performance evaluation

• First, a block-based approach for steganalysis which exploits the characteristics of image blocks.
• Second, performance study on block-based image steganalysis in terms of block sizes and block numbers.
• Finally, extensive performance evaluation of block-based image steganalysis decision fusion for block-based image steganalysis.

Traditional image steganalysis is conducted with respect to the entire image frame. In this work, we differentiate a stego image from its cover image based on steganalysis of decomposed image blocks. After image decomposition into smaller blocks, we classify image blocks into multiple classes and find a classifier for each class. Then, steganalysis of the whole image can be obtained by integrating results of all image blocks via decision fusion. Extensive performance evaluation of block-based image steganalysis is conducted. For a given test image, there exists a trade-off between the block size and the block number. We propose to use overlapping blocks to improve the steganalysis performance. Additional performance improvement can be achieved using different decision fusion schemes and different classifiers. Besides the block-decomposition framework, we point out that the choice of a proper classifier plays an important role in improving detection accuracy, and show that both the logistic classifier and the Fisher linear discriminant classifier outperforms the linear Bayes classifier by a significant margin.