中文摘要：

为了有效区分PS图像（经过常见图像处理操作得到的图像）和隐写图像,提高隐写检测的正确率,该文分析了隐写和PS这两类操作不同的噪声模型,并给出了一类基于图像噪声模型和特征联合的检测算法.该算法基于小波分解和小波滤波,分别得到待检测图像的小波系数子带和噪声小波系数子带,从这两类子带中分别提取直方图特征函数绝对矩,并将这两部分统计矩联合作为特征,最后采用BP神经网络分类器进行图像分类.在特征选取方面,文中对两类常用典型特征：概率密度函数矩和特征函数矩,基于高斯分布模型证明了对噪声小波子带系数,提取特征函数绝对矩优于概率密度函数绝对矩.基于LSB、LTSB、SLSB、PMK等隐写图像和锐化、对比度增强、添加标签等类型PS图像的实验表明：该算法能够有效区分原始图像和非原始图像,并能对PS图像和隐写图像进行较为可靠的分类检测.

英文摘要：

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.