基于小波子带特征函数矩和主成分分析的图像隐写分析方法  

A Image Steganalysis Method Based on Characteristic Function Moments of Wavelet Subbands and PCA

作　　者：孙子文[1] 周治平[1] 李慧[1] 

机构地区：[1]江南大学通信与控制工程学院,江苏无锡214122

出　　处：《信息网络安全》2009年第7期41-43,46,共4页Netinfo Security

摘　　要：针对图像隐写提出了基于小波特征函数矩的通用隐写分析方法。取图像的三级小波分解及第一级对角子带的再一次小波分解共17个子带的前三阶特征函数统计矩共51维特征,为更好地提高检测性能;再对图像的预测误差图像做相同的分解,求得同样子带的51维特征函数统计矩,共计102维特征;基于K-L变换的主成员分析方法进行特征降维;经支持向量机分类进行隐写分析。仿真结果显示提出的方法对JPHide和Jstego隐写有较好的检测性能。In this paper a universal steganalysis scheme is proposed for images. The scheme is based on the characteristic function moments of three-level wavelet subbands including the further decomposition coefficients of the first scale diagonal subband. The first three statistical moments of are selected to form 51 dimensional features for steganalysis. The same decomposition has been down to the predicted error image, and the first three statistical moments of each band are calculated to form 51 dimensional features. So total 102 dimentional features are obtained. Based on K-L conversation, the features are reduced by PCA and the SVM is adopted as the classifier. The simulation results show the proposed scheme has good performance in attacking JHide and Jstego.

关 键 词：信息科学与系统科学基础学科 隐写分析 特征函数 统计矩 主成分分析 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]