Universal steganalysis method based on multi-domain features
Yan, Yan; Li, Liting; Zhang, Qiuyu
2013-05-01
发表期刊Journal of Information and Computational Science
ISSN15487741
卷号10期号:7页码:2177-2185
摘要A new method of universal steganalysis for BMP images, which had low embedding rate and based on multi-domain features, is proposed in this paper. It provided a way to extract statistical features from multi-domain for universal steganalysis and solved the problem of low detection rate for a small amount of data embedding. Features were extracted from gradient energy differences in spatial domain, correlation coefficient in DCT domain, and the mean and standard deviation of difference value matrix in DWT domain. Experiments results show that detection achieved a better reliability when the embedding capacity is above 2 KB, compared with existing methods. © 2013 by Binary Information Press.
关键词Algorithms Support vector machines Correlation coefficient Difference values Embedding capacity Embedding rates Mean and standard deviations Multi-domain features Statistical features Universal steganalysis
DOI10.12733/jics20101693
收录类别EI
语种英语
出版者Binary Information Press, Flat F 8th Floor, Block 3, Tanner Garden, 18 Tanner Road, Hong Kong
EI入藏号20132316396475
EI主题词Mathematical techniques
EI分类号723 Computer Software, Data Handling and Applications - 921 Mathematics
来源库Compendex
分类代码723 Computer Software, Data Handling and Applications - 921 Mathematics
引用统计
文献类型期刊论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/112912
专题计算机与通信学院
作者单位School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
第一作者单位兰州理工大学
第一作者的第一单位兰州理工大学
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Yan, Yan,Li, Liting,Zhang, Qiuyu. Universal steganalysis method based on multi-domain features[J]. Journal of Information and Computational Science,2013,10(7):2177-2185.
APA Yan, Yan,Li, Liting,&Zhang, Qiuyu.(2013).Universal steganalysis method based on multi-domain features.Journal of Information and Computational Science,10(7),2177-2185.
MLA Yan, Yan,et al."Universal steganalysis method based on multi-domain features".Journal of Information and Computational Science 10.7(2013):2177-2185.
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