Online universal steganalysis system based on multiple pre-trained model
In reality,universal blind steganalysis is still a sensitive issue.A universal online steganalysis system that could be used in practical application was proposed.With reducing the dimensions of SRM,it could improve availability and speed up feature extraction.Some effective pre-trained models and w...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2017-05-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2017.00164 |
| _version_ | 1811339653625675776 |
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| author | Ya-fei YUAN,Wei LU Bing-wen FENG,Jian WENG |
| author_facet | Ya-fei YUAN,Wei LU Bing-wen FENG,Jian WENG |
| author_sort | Ya-fei YUAN,Wei LU |
| collection | DOAJ |
| description | In reality,universal blind steganalysis is still a sensitive issue.A universal online steganalysis system that could be used in practical application was proposed.With reducing the dimensions of SRM,it could improve availability and speed up feature extraction.Some effective pre-trained models and weighted voting strategy were used in this system with a B/S architecture,involving a higher speed.In addition,multithread technology was introduced.Experimental results demonstrate that high detection accuracy can be obtained and about 0.97 seconds for single detection with the system. |
| first_indexed | 2024-04-13T18:30:02Z |
| format | Article |
| id | doaj.art-f53fe63f5fb44e1d8b17b004ccf963fc |
| institution | Directory Open Access Journal |
| issn | 2096-109X |
| language | English |
| last_indexed | 2024-04-13T18:30:02Z |
| publishDate | 2017-05-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj.art-f53fe63f5fb44e1d8b17b004ccf963fc2022-12-22T02:35:07ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2017-05-0135323710.11959/j.issn.2096-109x.2017.00164Online universal steganalysis system based on multiple pre-trained modelYa-fei YUAN,Wei LU0Bing-wen FENG,Jian WENG1School of Data and Computer Science,Sun Yat-sen University,Guangzhou 510006,China College of Information Science and Technology,Jinan University,Guangzhou 510632,ChinaIn reality,universal blind steganalysis is still a sensitive issue.A universal online steganalysis system that could be used in practical application was proposed.With reducing the dimensions of SRM,it could improve availability and speed up feature extraction.Some effective pre-trained models and weighted voting strategy were used in this system with a B/S architecture,involving a higher speed.In addition,multithread technology was introduced.Experimental results demonstrate that high detection accuracy can be obtained and about 0.97 seconds for single detection with the system.http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2017.00164digital image steganographysteganalysismulti-modelweighted votingonline detection |
| spellingShingle | Ya-fei YUAN,Wei LU Bing-wen FENG,Jian WENG Online universal steganalysis system based on multiple pre-trained model 网络与信息安全学报 digital image steganography steganalysis multi-model weighted voting online detection |
| title | Online universal steganalysis system based on multiple pre-trained model |
| title_full | Online universal steganalysis system based on multiple pre-trained model |
| title_fullStr | Online universal steganalysis system based on multiple pre-trained model |
| title_full_unstemmed | Online universal steganalysis system based on multiple pre-trained model |
| title_short | Online universal steganalysis system based on multiple pre-trained model |
| title_sort | online universal steganalysis system based on multiple pre trained model |
| topic | digital image steganography steganalysis multi-model weighted voting online detection |
| url | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2017.00164 |
| work_keys_str_mv | AT yafeiyuanweilu onlineuniversalsteganalysissystembasedonmultiplepretrainedmodel AT bingwenfengjianweng onlineuniversalsteganalysissystembasedonmultiplepretrainedmodel |