基于差分零系数和索引共生矩阵的通用隐密分析  

Universal Steganalysis Based on Differential Zero Coefficients and Index Co-Occurrence Matrix

作　　者：巩锐[1] 王宏霞[1] 

机构地区：[1]西南交通大学信息科学与技术学院,四川成都610031

出　　处：《软件学报》2013年第12期2909-2920,共12页Journal of Software

基　　金：国家自然科学基金(61170226);中央高校基本科研业务费专项资金(SWJTU11CX047;SWJTU12ZT02);四川省青年科技创新研究团队项目(2011JTD0007);成都市科技计划(12DXYB214JH-002)

摘　　要：为提高互联网通信的安全性和可靠性,提出一种针对图形交换格式(graphics interchange format,简称GIF)图像的隐密分析算法.该算法基于差分零系数(differential zero coefficients,简称DZC)和索引共生矩阵(index cooccurrence matrix,简称ICM),提取对图像像素间颜色相关性和图像纹理特征变化敏感的36维统计特征.结合支持向量机(support vector machine,简称SVM)分类技术,实现对GIF图像中隐密信息的有效检测.实验结果表明,相比于同类算法,该算法对最佳奇偶分配(optimum parity assignment,简称OPA)、分量和(sum of components,简称SoC)、多比特分配(multibit assignment steganography,简称MBA)等典型隐密算法以及EzStego,S-Tools4,Gif-it-up等网络上常见隐密工具的检测效果更佳,时间效率更高,且具备通用隐密分析的能力.To improve the security and reliability of Internet communications, a steganalysis algorithm for graphics interchange format （GIF） images is proposed in this paper. 36-dimensional statistical features of GIF image, which are sensitive to the color correlation between adjacent pixels and the breaking of image texture, are extracted based on differential zero coefficients （DZC） and index co-occurrence matrix （ICM）. Support vector machine （SVM） technique takes the 36-dimensional statistical features to detect hidden message in GIF images effectively. Experimental results indicate that the proposed algorithm has better detection performance and higher time efficiency comparing with other similar steganalysis algorithms for typical steganographic algorithms including optimum parity assignment （OPA）, sum of components （SoC）, multibit assignment steganography （MBA） and steganographic tools which are popular in the Internet, such as EzStego, S-Tools4 and Gif-it-up. Furthermore, the proposed algorithm has the ability of universal steganalysis.

关 键 词：隐密分析 GIF图像 差分零系数 索引共生矩阵 支持向量机 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]