Color image steganalysis based on residuals of channel differences

Abstract

This study proposes a color image steganalysis algorithm that extracts high-dimensional rich model features from the residuals of channel differences. First, the advantages of features extracted from channel differences are analyzed, and it shown that features extracted in this manner should be able to detect color stego images more effectively. A steganalysis feature extraction method based on channel differences is then proposed, and used to improve two types of typical color image steganalysis features. The improved features are combined with existing color image steganalysis features, and the ensemble classifiers are trained to detect color stego images. The experimental results indicate that, for WOW and S-UNIWARD steganography, the improved features clearly decreased the average test errors of the existing features, and the average test errors of the proposed algorithm is smaller than those of the existing color image steganalysis algorithms. Specifically, when the payload is smaller than 0.2 bpc, the average test error decreases achieve 4% and 3%.