计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (21): 97-99.DOI: 10.3778/j.issn.1002-8331.2010.21.027

• 网络、通信、安全 • 上一篇    下一篇

基于改进支持向量机的隐写分析方法

陈晓楠1,张敏情1,马 林2   

  1. 1.武警工程学院 电子技术系,网络与信息安全武警部队重点实验室,西安 710086
    2.武警工程学院 装备运输系,西安 710086
  • 收稿日期:2010-02-22 修回日期:2010-05-17 出版日期:2010-07-21 发布日期:2010-07-21
  • 通讯作者: 陈晓楠

Steganalysis method based on improved SVM

CHEN Xiao-nan1,ZHANG Min-qing1,MA Lin2   

  1. 1.Network and Information Security Key Lab,Electronics Department,Engineering College of the APF,Xi’an 710086,China
    2.Equipment and Transport Department,Engineering College of the APF,Xi’an 710086,China
  • Received:2010-02-22 Revised:2010-05-17 Online:2010-07-21 Published:2010-07-21
  • Contact: CHEN Xiao-nan

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Fridrich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。

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

Abstract: 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.

Key words: steganalysis, feature extraction, Least Square Hyper Sphere One-Class SVM(LSHS-OCSVM), classification

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