Steganalysis Algorithm Based on the D Reduction of Improved Differential Matrix in Images

Aimed to improve the detection efficiency of information-hiding blind detection system, the present study proposes an SADRID-I image steganalysis algorithm that is based on improved differential matrix, according to the high dimensions and correlation of image features. Using attribute significance and differential matrix that stems from rough sets theory, the algorithm can implement D attributes reduction to high dimensional statistic features extracted from test image to get more accurate rough features. By screening and extraction, it reduces feature dimension and computational complexity, and correlations between eigenvectors are scientifically counteracted, with an improvement in detection efficiency of information-hiding blind detection system. Using the proposed algorithm to reducte the statistical features extracted from different image databases, image formats and different scales, and SVM classifier employed in the tests of detecting Steganographic tools such as Cox, Piva, jphide, MB1 and MB2, etc, a large body of experimental results indicate that SADRID-I steganalysis algorithm has better universality, stability and effectiveness while it gets significant improvement in space efficiency, time efficiency, feature accuracy, etc.