Image Steganalysis Based on Statistical Moments of Wavelet Subband Histograms in Different Frequencies and Support Vector Machine

This paper proposed a new image steganalysis scheme based on statistical moments of histogram of multi-level wavelet subbands in frequency domain. Different frequencies of histogram have different sensitivity to various data embedding. Then we decompose the test image using three-level Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LLO subband). The DFT of each subband, is calculated. It is divided into low and high frequency bands. The first three statistical moments of each band are selected to form a 78-dimensional feature vector for Steganalysis. Support vector machines (SVM) classifier is then used to discriminate between stego- images and innocent images. Experimental results show that the proposed algorithm outperforms previously existing techniques.