Wavelet domain audio steganalysis based on statistical moments and PCA

A wavelet domain audio steganalysis method based on principal component analysis (PCA) is proposed. The audio signal is firstly decomposed by 4-level discrete wavelet transform, then the 36 statistical moments of the histogram and the frequency domain histogram for both the audio signal and its wavelet subbands are calculated as features, then the preprocessing of PCA is used on the statistics features and radial basis function (RBF) network is utilized as a classifier. The proposed scheme not only reduces the dimension of the feature vector effectively and simplifies the design of the classifier, but also keeps the detection performance. Then this scheme is utilized to defect the stego-audio signals embedded by wavelet domain LSB, quantization index method (QIM) and addition method (AM). Simulation results show that the performance of our scheme is better than that of the scheme proposed by Xuemin Ru and the detection rates are all greater than 92%.