k均值聚类的混合异构图像隐写分析

doi:10.3969/j.issn.0255-8297.2014.05.017

应用科学学报 ›› 2014, Vol. 32 ›› Issue (5): 543-550.doi: 10.3969/j.issn.0255-8297.2014.05.017

• 信号与信息处理 • 上一篇

k均值聚类的混合异构图像隐写分析

谢凯，张涛，奚玲，李文祥，平西建

解放军信息工程大学信息系统工程学院，郑州450001

收稿日期:2013-10-25 修回日期:2014-03-04 出版日期:2014-09-23 发布日期:2014-03-04
作者简介:张涛，副教授，研究方向：信息隐藏、图像处理、模式识别，E-mail：brunda@163.com；平西建，教授，博导，研究方向：信息隐藏、图像处理、模式识别，E-mail: pinxijian@163.com
基金资助:
国家自然科学基金(No.61272490, No.60903221) 资助

Steganalysis of Heterogeneous Images Using k-Means Clustering

XIE Kai, ZHANG Tao, XI Ling, LI Wen-xiang, PING Xi-jian

Institute of Information System Engineering, PLA Information Engineering University, Zhengzhou 450001, China

Received:2013-10-25 Revised:2014-03-04 Online:2014-09-23 Published:2014-03-04

摘要/Abstract

摘要： 提出了一种基于k均值聚类的混合异构图像隐写分析算法. 在训练阶段，根据图像纹理复杂度对图像库
进行聚类，并针对每一类图像训练相应的分类器. 在测试阶段，根据测试图像的纹理复杂度对其进行类别判断，然
后送至相应类别的分类器中进行隐写检测，从而减弱了失配状态对现有隐写分析算法造成的影响. 实验结果表明，
该算法较好地提高了现有隐写分析算法的检测精度.

关键词: 信息隐藏, 图像隐写分析, k均值聚类, 图像纹理复杂度

Abstract: A new image steganalysis method using k-means clustering is presented. In the training phase,
the input images are classified to several classes using k-means clustering according to texture complexity.
The training process is specialized for each class separately. In the testing phase, the given test image is first
classified to the class it belongs to according to its texture complexity. It is then submitted to the corresponding
steganalysis classifier. The proposed method can reduce mismatch penalty considerably. Experimental results
demonstrate that the method can significantly enhance detection accuracy of existing steganalysis methods.

Key words: information hiding, k-means clustering, image texture complexity, image steganalysis

中图分类号:

TP391

谢凯，张涛，奚玲，李文祥，平西建. k均值聚类的混合异构图像隐写分析[J]. 应用科学学报, 2014, 32(5): 543-550.

XIE Kai, ZHANG Tao, XI Ling, LI Wen-xiang, PING Xi-jian. Steganalysis of Heterogeneous Images Using k-Means Clustering[J]. Journal of Applied Sciences, 2014, 32(5): 543-550.

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k均值聚类的混合异构图像隐写分析

Steganalysis of Heterogeneous Images Using k-Means Clustering

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