| 时晨曦,张敏情.基于改进增强特征选择算法的特征融合图像隐写分析[J].光电子激光,2014,(3):551~557 |
| 基于改进增强特征选择算法的特征融合图像隐写分析 |
| Image steganalysis based on feature fusion by improved boosting feature selectio n algorithm |
| 投稿时间：2013-06-22 |
| DOI： |
| 中文关键词: 隐写分析 特征融合 特征选择 改进的增强特征选择(BFS)算法 特征相关性 |
| 英文关键词:steganalysis feature fusion feature selection improved boosting feature selec tion (BFS) algorithm feature relevance |
| 基金项目:国家自然科学基金(61379152)和陕西省自然科学基金基础研究(2012JIM8014)资助项目 (武警工程大学 电子技术系,陕西 西安 710086) |
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| 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 steg analysis 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 aspects of linear and nonlinear correlations.Statistical performance 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 impro ved.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,i ncluding F5,Outguess and MME3.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 gr eater universality. |
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