The steganalysis method based on sparing statistics of short duplicate code is an efficient universal detection algorithm for LSB matching steganography. But the steganalysis method must select the appropriate dimension of short duplicate code to meet the different applications. This one-dimensionstatistical analysis method could not take into account the links between multivari-ate statistical features, thus may affect the detection capability. In this paper, a detection capability law of a single short duplicate code statistical feature is proved, and a method to reasonable choice of the short duplicate code dimension is presented to reduce the detection number. By analyzing the correlation between the statistical features of short duplicate code spacing statistics, a selected feature subset is described as a vector of local features. Then, a universal steganalysis method based on local features extracted from LSB sequences is proposed. The proposed steganalysis method uses the Gaussian Mixture Model (GMM) to describe the multi-dimensional local features, and designs classifier by integrating GMM generative model and SVM discriminative method based on global sequence vocabulary. The experimental results show that under the premise of effective control of the false alarm rate, the proposed method achieves the best overall detection performance to LSB matching steganography and to LSB replacement steganography.%基于短重码间距统计的隐写检测方法对LSB匹配等隐写技术有良好的检测性能.然而该方法为适应不同的应用场合,需要选择适当的短重码维数.这种一元统计分析方法无法考虑多个特征之间存在的联系,从而影响检测能力.本文分析证明了单个短重码间距统计变量的检测能力规律,给出了可减少检测次数的合理选择短重码维数的方法.基于短重码间距统计特征变量之间的相关性选择特征子集,构造局部特征描述向量,进而提出一种基于LSB序列局部特征的通用隐写检测方法.该方法采用GMM生成模型描述多维局部特征,并基于全局序列词汇设计融合GMM生成模型与SVM判别方法的分类器.实验结果表明:本文方法在有效控制虚警率的前提下,对LSB匹配隐写和LSB替换隐写都有较好的检测性能.
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