Block-based image steganalysis for a multi-classifier
Traditional image steganalysis techniques for classification of steganograhic algorithms are conducted with respect to the entire image. In this work, we aim to design a multi-classifier which classifies stego images depending on their steganographic algorithms in addition to distinguishing stego images from cover images. This classification is 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 image or a stego image with a specific steganographic algorithm. Consequently, the steganalysis of the whole image can be conducted by fusing weighted steganalysis results of all image blocks through a voting process. Experimental results will be given to show the advantage of using the proposed block-based image steganalysis for a multi-classifier.