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基于改进支持向量机的隐写分析方法

Steganalysis method based on improved SVM
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摘要 为了更有效地提高图像隐写分析的速度和正确检测率,提出了一种基于改进的支持向量机的隐写分析方法。采用Frid-rich提出的多特征融合提取算法对图像进行特征提取,克服了单一特征不能很好描述图像差别的不足。然后提出了一种将最小二乘法与超球体一类支持向量机(HSOC-SVM)相结合的分类器——最小二乘超球一类支持向量机(LSHS-OCSVM),并与目前广泛使用的FLD和非线性SVM分类器作对比实验。结果表明,方法是一种有效、高速的隐写分析方法。 To enhance the speed and correct examination rate of image steganalysis,this paper provides a new steganalysis method based on the improved SVM.It uses mixture of a few features discussed by Fridrich to extract the features of images,and overcomes the shortcomings that using only one feature can not present image differences well.Then a new classification,Least Square Hyper Sphere One-Class SVM（LSHS-OCSVM） which combines least square programme and the sphere one-class SVM,is provided.Compared with FLD and nonlinear SVM widely used at present,the experiment results prove that it is an effective steganalysis method with high-speed detection.
作者 陈晓楠 张敏情 马林
机构地区 武警工程学院电子技术系 武警工程学院装备运输系
出处 《计算机工程与应用》 CSCD 北大核心 2010年第21期97-99,共3页 Computer Engineering and Applications
基金 国家自然科学基金No.60842006~~
关键词 隐写分析 特征提取 最小二乘超球一类支持向量机 分类器 steganalysis feature extraction Least Square Hyper Sphere One-Class SVM（LSHS-OCSVM） classification
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献10

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计算机工程与应用
2010年 第21期

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