Independent component analysis applied to steganalysis

Universal steganalysis techniques attempt to detect hidden information without knowledge about the steganographic methods. One of the mast important things is to find feature sets, which are sensitive to the embedding process. Whether these features are "good" directly influence the accuracy of detection. This paper describes an approach to define sensitive feature sets using ICA (independent component analysis) decomposition and prediction in order to build statistical models of image independent component. Kernel-SVM is then used to discriminate between stego-images and cover-images.