An Approach of Detecting Least Significant Bit Matching Based on Image Content

Recently, it is a new direction to improve the performance of image steganalysis by combining the detection of information hidden with image content analysis. Relative to methods depending on entire image, this paper analyzes the effect of LSB (Least Significant Bit) matching steganography on image sub-areas, and presents a novel steganalyzer based on the combined discrimination. Firstly, the images are divided into several sub-areas according to the image content complexity. Secondly, the histogram spectral features of pixel sequence of each sub-area are extracted by using two different filters. Then, the Bayes classifiers are trained respectively by features of each class of sub-area in order to obtain its weights. Finally, each sub-area of a test image is detected depending on its class and the final discrimination result of the test image is achieved by weighted fusion of the results of its sub-areas. Experimental results show that the proposed method exhibits excellent performance for the detection of LSB matching, outperforms existing representative approaches.