Digital image steganalysis based on local textural features and double dimensionality reduction

This work proposes a spatial steganalysis scheme based on local textural features and double dimensionality reduction. First, an image is filtered by multiple filters to obtain a number of residual images. Local textural patterns are obtained by comparing the pixel values with the neighbors' value in each residual image. By combining all local textural patterns, a high-dimensional textural feature set is formed. Then, principal component analysis is used to perform double dimensionality reduction for high-dimensional textural features. In the first dimensionality reduction stage, the correlation from the same filter is eliminated, while the correlation from different filters can be also eliminated in the second dimensionality reduction stage. Finally, a textural feature set with low dimensionality is proposed and can be effectively used in steganalysis. Experimental results show that proposed textural feature set can efficiently detect adaptive steganographic schemes in spatial domain. Copyright © 2014 John Wiley & Sons, Ltd.