Research On Universal Steganalysis For Color Images Based On Feature Analysis

Steganalysis is an important branch of information hiding detection which can be divided into two categories:specific steganalysis and universal steganalysis.Specific steganalysis has higher detection accuracy but it is not practical owing to the diversity of steganography,while universal steganalysis can be trained on any Steganographic method, so it has a better adaptability.Consequently the research on universal steganalysis is a matter of great significance.Universal steganalysis is actually similar to pattern classification which centers two-class classification.The goal of it is to divide the image into two groups:cover image and stego-image.So the extraction and optimizing of features and the design of the classifier are three crucial technologies in it.This thesis carries a deep study on those three parts through a great deal of experiments and acquires a series of valuable results which can be summarized in to the following aspects:First,it is found that recent universal steganalysis schemes usually extract the features in gray images,in this way they neglect the chrominance information of images.On the basis of the comparison and analyzing between high order probability density function moments of wavelet coefficients and characteristic function moments of histogram,a universal steganalysis combining color correlation degree and characteristic function moments of histogram is proposed which extracts the features from both luminance and chrominance components of images.Moments of wavelet characteristic function is introduced to replace the wavelet high-order statistics for luminance component of image and vector directional correlation degree is calculated for chrominance component.This method overcomes the unilateralism of extracting features in grayscale images and improves the effectiveness of feature vector by introducing CF moments.The experimental results have shown that the proposed method offers a better performance over several Steganographic methods.Second,considering that some features are not able to reflect well the statistical change of the setgo-images.So a feature evaluation strategy based on ANOV(Analysis of Variance)is proposed.By exploring it,the features which are more sensitive to hidden message are selected.The results have shown that after ANOV the dimension of the feature vector reduces to thirty two and the detection accuracy has been effectively improved too.Third,take into account that most of the recent universal steganalysis schemes are two class steganalysis,so they can only judge the existence of the hidden information.Here we introduce our method into a multi-class steganalysis by using directed acyclic graph SVM(support vector machine) algorithm,This method is not only able to tell whether a image is stego or not,but also can reliably classifies it to its embedding tool.Provide effective information for specific steganalysis.The results have indicated that this method has a better classification result and makes a helpful attempt for combination of universal steganalysis and specific steganalysis.