Image Steganalysis Based on Mutual Information and Feature Fusion
Zhang Minqin · Journal of Wuhan University · 2013
A new universal steganalysis scheme based on weighting fusion to attack JPEG(joint photographic experts group)steganography is presented to improve detection rates and efficiency.Firstly,difference arrays within and between DCT(discrete cosine transform)blocks along horizontal,vertical,zigzag directions are computed.Then,joint probability density matrix is applied to capture the impacts on the relevance of DCT coefficients caused by embedding process.And features from three directions are constructed.Finally,weights of each feature are quantified using the mutual information between features and classification classes.Final features vectors for steganalysis are derived by fusing the weighted features and are tested by SVM(support vector machine).The experimental results show that the proposed scheme provides reliable detection rates in attacking three steganographic schemes including F5,Outguess and MB2.The detection rates are higher than 88.4%,and the feature fusion method increases the detection efficiency.