中文摘要：

针对现有的基于特征融合的JPEG隐写分析方法特征冗余度高、通用性较低的问题，提出了一种基于改进的增强特征选择（BFS, boosting feature selection）算法的通用JPEG隐写分析方法。从线性相关度和非线性相关度两方面降低特征冗余，将特征自相关系数和互信息这两种统计性能引入到特征的评价准则中，重新设计了特征权重计算方法，改进了BFS算法的特征评价函数。通过改进的BFS特征选择算法将3组互补性较强且准确率高的特征进行融合降维，得到最优特征子集训练分类器。对3种高隐蔽性隐写算法F5、Outguess和MME3，在不同嵌入率下进行了大量实验。结果表明，本文方法的分析准确率高于现有的检测率较高的JPEG隐写分析方法和典型的融合分析方法，融合后的特征相关性明显下降，并且具有更强的通用性。

英文摘要：

In view of the problems in the existing feature fusion based JPEG steganalysis schemes, such as high redundancy in selected features and weak universality, a universal JPEG steganalysis approach based on improved boosting feature selection （BFS） method is presented, Feature redundancy is reduced in as- pects of linear and nonlinear correlations. Statistical performanee including auto-correlation coefficients and mutual information is introduced in feature evaluation rules. The algorithm of computing feature weighting is redesigned. The feature evaluation function of BFS is improved. Three complementary sets of features that have high detection accuracy are fused using the improved BFS algorithm. The selected optimal feature subset is used for training classifiers. Experiments are done in various embedding rates for three steganographic schemes with high concealment,including F5, Outguess and MMEd. The results show that the detection accuracy of the proposed scheme is higher than that of some existing JPEG steganalysis approaches and some classical fusion methods. The fused features by improved BFS have lower correlation and this scheme has greater universality.