Neural Linguistic Steganalysis via Multi-Head Self-Attention
Linguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attenti...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6668369 |
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| _version_ | 1832565995578851328 |
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| author | Sai-Mei Jiao Hai-feng Wang Kun Zhang Ya-qi Hu |
| author_facet | Sai-Mei Jiao Hai-feng Wang Kun Zhang Ya-qi Hu |
| author_sort | Sai-Mei Jiao |
| collection | DOAJ |
| description | Linguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attention. In the proposed steganalysis approach, words in text are firstly mapped into semantic space with a hidden representation for better modeling the semantic features. Then, we utilize multi-head self-attention to model the interactions between words in carrier. Finally, a softmax layer is utilized to categorize the input text as cover or stego. Extensive experiments validate the effectiveness of our approach. |
| format | Article |
| id | doaj-art-60201bf1ba4146188506716f094cfa76 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-60201bf1ba4146188506716f094cfa762025-02-03T01:05:20ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/66683696668369Neural Linguistic Steganalysis via Multi-Head Self-AttentionSai-Mei Jiao0Hai-feng Wang1Kun Zhang2Ya-qi Hu3College of Computer Science and Technology, Hainan Tropical Ocean University, Sanya, Hainan 572022, ChinaCollege of Computer Science and Technology, Hainan Tropical Ocean University, Sanya, Hainan 572022, ChinaEducation Center of MTA, Hainan Tropical Ocean University, Sanya, Hainan 572022, ChinaCollege of Computer Science and Technology, Hainan Tropical Ocean University, Sanya, Hainan 572022, ChinaLinguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attention. In the proposed steganalysis approach, words in text are firstly mapped into semantic space with a hidden representation for better modeling the semantic features. Then, we utilize multi-head self-attention to model the interactions between words in carrier. Finally, a softmax layer is utilized to categorize the input text as cover or stego. Extensive experiments validate the effectiveness of our approach.http://dx.doi.org/10.1155/2021/6668369 |
| spellingShingle | Sai-Mei Jiao Hai-feng Wang Kun Zhang Ya-qi Hu Neural Linguistic Steganalysis via Multi-Head Self-Attention Journal of Electrical and Computer Engineering |
| title | Neural Linguistic Steganalysis via Multi-Head Self-Attention |
| title_full | Neural Linguistic Steganalysis via Multi-Head Self-Attention |
| title_fullStr | Neural Linguistic Steganalysis via Multi-Head Self-Attention |
| title_full_unstemmed | Neural Linguistic Steganalysis via Multi-Head Self-Attention |
| title_short | Neural Linguistic Steganalysis via Multi-Head Self-Attention |
| title_sort | neural linguistic steganalysis via multi head self attention |
| url | http://dx.doi.org/10.1155/2021/6668369 |
| work_keys_str_mv | AT saimeijiao neurallinguisticsteganalysisviamultiheadselfattention AT haifengwang neurallinguisticsteganalysisviamultiheadselfattention AT kunzhang neurallinguisticsteganalysisviamultiheadselfattention AT yaqihu neurallinguisticsteganalysisviamultiheadselfattention |