Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박종일 | - |
dc.date.accessioned | 2022-03-28T02:29:46Z | - |
dc.date.available | 2022-03-28T02:29:46Z | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | Proceedings of International Conference on Smart Computing and Cyber Security, page. 365-373 | en_US |
dc.identifier.isbn | 978-981-15-7990-5 | - |
dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-981-15-7990-5_36 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/169443 | - |
dc.description.abstract | In general, image deformations caused by different steganographic algorithms are extremely small and of high similarity. Therefore, detecting and identifying multiple steganographic algorithms are not easy. Although recent steganalytic methods using deep learning showed highly improved detection accuracy, they were dedicated to binary classification, i.e., classifying between cover images and their stego images generated by a specific steganographic algorithm. In this paper, we aim at achieving quinary classification, i.e., detecting (=classifying between stego and cover images) and identifying four spatial steganographic algorithms (LSB, PVD, WOW, and S-UNIWARD), and propose to use a hierarchical structure of convolutional neural networks (CNN) and residual neural networks (ResNet). Experimental results show that the proposed method can improve the classification accuracy by 17.71% compared to the method that uses a single CNN. | en_US |
dc.description.sponsorship | This work was supported by the research fund of the Signal Intelligence Research Center supervised by the Defense Acquisition Program Administration and Agency for the Defense Development of Korea. | en_US |
dc.language.iso | en | en_US |
dc.publisher | SPRINGER | en_US |
dc.subject | Steganalysis | en_US |
dc.subject | Image steganography | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Residual neural network | en_US |
dc.subject | Hierarchical structure | en_US |
dc.subject | Quinary classification | en_US |
dc.title | Classification of Multiple Steganographic Algorithms Using Hierarchical CNNs and ResNets | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/978-981-15-7990-5_36 | - |
dc.relation.page | 365-373 | - |
dc.contributor.googleauthor | Kang, Sanghoon | - |
dc.contributor.googleauthor | Park, Hanhoon | - |
dc.contributor.googleauthor | Park, Jong-Il | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF COMPUTER SCIENCE | - |
dc.identifier.pid | jipark | - |
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