JPEG steganalysis based on Tri-training semi-supervised learning
A JPEG steganalytic method based on semi-supervised learning algorithm was presented.Using three catego-ries of statistical features for JPEG images and multiple hyperspheres one-class SVM,three classifiers were generated from the original labeled example set.These classifiers were then refined usin...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2008-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74654466/ |
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| _version_ | 1841537540565237760 |
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| author | GUO Yan-qing 1 KONG Xiang-wei1 YOU Xin-gang1 HE De-quan1 |
| author_facet | GUO Yan-qing 1 KONG Xiang-wei1 YOU Xin-gang1 HE De-quan1 |
| author_sort | GUO Yan-qing 1 |
| collection | DOAJ |
| description | A JPEG steganalytic method based on semi-supervised learning algorithm was presented.Using three catego-ries of statistical features for JPEG images and multiple hyperspheres one-class SVM,three classifiers were generated from the original labeled example set.These classifiers were then refined using unlabeled examples in the Tri-training process,which could effectively improve detecting ability by exploiting a large amount of unlabeled images.Experimen-tal results showed the effectiveness of our proposed method. |
| format | Article |
| id | doaj-art-5b1279b0fcab4446b809d29e5dd27393 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2008-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5b1279b0fcab4446b809d29e5dd273932025-01-14T08:32:13ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2008-01-0120520974654466JPEG steganalysis based on Tri-training semi-supervised learningGUO Yan-qing 1KONG Xiang-wei1YOU Xin-gang1HE De-quan1A JPEG steganalytic method based on semi-supervised learning algorithm was presented.Using three catego-ries of statistical features for JPEG images and multiple hyperspheres one-class SVM,three classifiers were generated from the original labeled example set.These classifiers were then refined using unlabeled examples in the Tri-training process,which could effectively improve detecting ability by exploiting a large amount of unlabeled images.Experimen-tal results showed the effectiveness of our proposed method.http://www.joconline.com.cn/zh/article/74654466/steganalysissemi-supervised learningTri-trainingmultiple hypersphereone class-SVM |
| spellingShingle | GUO Yan-qing 1 KONG Xiang-wei1 YOU Xin-gang1 HE De-quan1 JPEG steganalysis based on Tri-training semi-supervised learning Tongxin xuebao steganalysis semi-supervised learning Tri-training multiple hypersphere one class-SVM |
| title | JPEG steganalysis based on Tri-training semi-supervised learning |
| title_full | JPEG steganalysis based on Tri-training semi-supervised learning |
| title_fullStr | JPEG steganalysis based on Tri-training semi-supervised learning |
| title_full_unstemmed | JPEG steganalysis based on Tri-training semi-supervised learning |
| title_short | JPEG steganalysis based on Tri-training semi-supervised learning |
| title_sort | jpeg steganalysis based on tri training semi supervised learning |
| topic | steganalysis semi-supervised learning Tri-training multiple hypersphere one class-SVM |
| url | http://www.joconline.com.cn/zh/article/74654466/ |
| work_keys_str_mv | AT guoyanqing1 jpegsteganalysisbasedontritrainingsemisupervisedlearning AT kongxiangwei1 jpegsteganalysisbasedontritrainingsemisupervisedlearning AT youxingang1 jpegsteganalysisbasedontritrainingsemisupervisedlearning AT hedequan1 jpegsteganalysisbasedontritrainingsemisupervisedlearning |