Detection of PS and Stego Images Based on Noise Models and Features Integration
Luo Xiang · Chinese Journal of Computers · 2010
In order to reliably classify the stego images and PS images,which are images modified by some normal image processing operation,and improve the detection accuracy of existing steganalysis algorithms,the different noise models of PS images and stego images are analyzed,and a detection algorithm based on noise models and features integration is proposed.First,the wavelet decomposition of images is made,and then a filtering operation is applied to obtain the wavelet subbands of noise images.Second,some high order absolute characteristic function(CF) moments of histogram are extracted from the wavelet coefficient subbands and their noise versions respectively.Third,these features are integrated as feature vectors.Last,a BP neural network is designed to detect images.In addition,two kinds of typical features,namely the probability density function(PDF) moments and CF moments,are analyzed,and the following conclusion is proved: for wavelet subbands of noise image,absolute CF moments are more sensitive to the changes of an image than absolute PDF moments.A series of experiments are made based on the stego images which embedded with methods such as LSB,LTSB,SLSB,PMK,and PS images with typical operations such as image sharpening,contrast enhancing,adding tags and so on.Experimental results show that the proposed method can effectively detect non-natural images from natural images,and can reliably classify images as stego image and PS image.