Steganalysis by subtractive pixel adjacency matrix and dimensionality reduction

Subtractive pixel adjacency matrix (SPAM) features, introduced by Pevn’y et al. as a type of Markov chain features, are widely used for blind steganalysis in the spatial domain. In this paper, we present three improvements to SPAM as follows: 1) new features based on parallel subtractive pixels are added to the SPAM features, which only refer to collinear subtractive pixels; 2) features are extracted not only from the spatial image, but also from its grayscale-inverted image, making the feature matrices symmetrical and reducing their dimensionality by about half; and 3) a new kind of adjacency matrix is used, thereby reducing about 3/4 of the dimensionality of the features. Experimental results show that these methods for dimensionality reduction are very effective and that the proposed features outperform SPAM.