A new universal blind steganalysis scheme is presented based on analysing the limitation of Farid's universal blind steganalysis on the dimension of the feature vector

the validity of the single feature

and the correlation of the features.Preprocessing is proposed using principle component analysis on the image statistics features and steganalysis classifier is constructed using RBF network.The scheme not only reduces the dimension of the feature vector enormously

but also improves the performance of the detection.Apply our scheme and Farid's scheme to detecting the stego images produced by JSteg

EZStego

and S-Tools respectively

and the comparison of these simulation results shows that after the preprocessing using principle component analysis the dimension of the feature vector in our scheme decreases 174(Jsteg)

163(EzStego)

180(S-Tools)

and therefore simplifies the design of the steganalysis classifier.Furthermore

our scheme is quite more efficient because the stego image that the proportion of the embedding message to the maximal embedding capability is more than 60%(Jsteg)

80%(EzStego)

50%(S-Tools) can be detected efficiently by our scheme.