{"title":"基于统计MWCF和f -评分法的BMP图像盲隐写分析方法","authors":"Xue Zhang, S. Zhong","doi":"10.1109/ICWAPR.2009.5207472","DOIUrl":null,"url":null,"abstract":"As the steganography technology becomes diversified recently, a good blind steganalyzer is in great request. In recent works, a blind steganalysis based on statistical moments of wavelet characteristic functions (MWCF) is proposed, but it has poor generalization ability to some extent. To improve this weakness, the F-score feature selection method is used to filter irrelevant, redundant features calculated from MWCF. By combining MWCF and F-score method, an improved blind steganalysis method is proposed in this article, called FS-MWCF method in short. Experimental results show that the FS-MWCF method has better generalization ability and lower classifying time complexity, less than half of that using MWCF method.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"148 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Blind steganalysis method for BMP images based on statistical MWCF and F-score method\",\"authors\":\"Xue Zhang, S. Zhong\",\"doi\":\"10.1109/ICWAPR.2009.5207472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the steganography technology becomes diversified recently, a good blind steganalyzer is in great request. In recent works, a blind steganalysis based on statistical moments of wavelet characteristic functions (MWCF) is proposed, but it has poor generalization ability to some extent. To improve this weakness, the F-score feature selection method is used to filter irrelevant, redundant features calculated from MWCF. By combining MWCF and F-score method, an improved blind steganalysis method is proposed in this article, called FS-MWCF method in short. Experimental results show that the FS-MWCF method has better generalization ability and lower classifying time complexity, less than half of that using MWCF method.\",\"PeriodicalId\":424264,\"journal\":{\"name\":\"2009 International Conference on Wavelet Analysis and Pattern Recognition\",\"volume\":\"148 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Wavelet Analysis and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICWAPR.2009.5207472\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Wavelet Analysis and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWAPR.2009.5207472","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Blind steganalysis method for BMP images based on statistical MWCF and F-score method
As the steganography technology becomes diversified recently, a good blind steganalyzer is in great request. In recent works, a blind steganalysis based on statistical moments of wavelet characteristic functions (MWCF) is proposed, but it has poor generalization ability to some extent. To improve this weakness, the F-score feature selection method is used to filter irrelevant, redundant features calculated from MWCF. By combining MWCF and F-score method, an improved blind steganalysis method is proposed in this article, called FS-MWCF method in short. Experimental results show that the FS-MWCF method has better generalization ability and lower classifying time complexity, less than half of that using MWCF method.