计算机应用研究2013，Vol.30Issue(7)：2105-2107,2127,4.DOI:10.3969/j.issn.1001-3695.2013.07.046
基于优化特征加权支持向量机的隐写分析方法
Steganalysis method based on optimized feature weighted SVM
摘要
Abstract
In order to solve the problem that the features dimensionalities extracting was too high and lack of independence,this paper proposed a new support vector machine based on dimensionality reduction and feature weighting.It adopted algorithms of principle component analysis(PCA)and information gain(IG)to accomplish images feature optimizing and acquire its weight matrix.Then it provided a new classification,optimized feature weighted support vector machine.Compared with currently widely used C-SVM classification in steganalysis,the experiment result proves that it is an effective method to reduce complexity of time.The algorithm has effective capability in steganalysis.关键词
隐写分析/主成分分析/信息增益/特征优化/特征加权/支持向量机Key words
steganalysis/principle component analysis(PCA)/information gain(IG)/feature optimizing/feature weigh-ting/support vector machine(SVM)分类
信息技术与安全科学引用本文复制引用
汪海涛,张卓,杨晓元,林志强..基于优化特征加权支持向量机的隐写分析方法[J].计算机应用研究,2013,30(7):2105-2107,2127,4.基金项目
国家自然科学基金资助项目(61103230) （61103230）
陕西省自然科学基金基础研究资助项目(2012JM8014) （2012JM8014）
