Anti-steganalysis for image on convolutional neural networks
Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Ba...
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| Veröffentlicht in: | Multimedia tools and applications 2020-02, Vol.79 (7-8), p.4315-4331 |
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| container_title | Multimedia tools and applications |
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| creator | Li, Shiyu Ye, Dengpan Jiang, Shunzhi Liu, Changrui Niu, Xiaoguang Luo, Xiangyang |
| description | Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based Attack; 2) LSB-Jstego Evolutionary Algorithms Based Attack. The experiment results show that the attack strategies could achieve 96.02% and 90.25% success ratio separately on the target CNN. The proposed attack scheme is an effective way to fool the CNN based steganalyzer and in addition demonstrates the vulnerability of the neural networks in steganalysis. |
| doi_str_mv | 10.1007/s11042-018-7046-6 |
| format | Article |
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While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based Attack; 2) LSB-Jstego Evolutionary Algorithms Based Attack. The experiment results show that the attack strategies could achieve 96.02% and 90.25% success ratio separately on the target CNN. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-86f059c19b54d20187b745ea20547ce6d5d30c3d69b5f8acfaf310ec981d3e2d3</citedby><cites>FETCH-LOGICAL-c316t-86f059c19b54d20187b745ea20547ce6d5d30c3d69b5f8acfaf310ec981d3e2d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-018-7046-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-018-7046-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Li, Shiyu</creatorcontrib><creatorcontrib>Ye, Dengpan</creatorcontrib><creatorcontrib>Jiang, Shunzhi</creatorcontrib><creatorcontrib>Liu, Changrui</creatorcontrib><creatorcontrib>Niu, Xiaoguang</creatorcontrib><creatorcontrib>Luo, Xiangyang</creatorcontrib><title>Anti-steganalysis for image on convolutional neural networks</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. 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While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based Attack; 2) LSB-Jstego Evolutionary Algorithms Based Attack. The experiment results show that the attack strategies could achieve 96.02% and 90.25% success ratio separately on the target CNN. The proposed attack scheme is an effective way to fool the CNN based steganalyzer and in addition demonstrates the vulnerability of the neural networks in steganalysis.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-018-7046-6</doi><tpages>17</tpages></addata></record> |
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| subjects | Artificial neural networks Computer Communication Networks Computer Science Data Structures and Information Theory Evolutionary algorithms Multimedia Information Systems Neural networks Special Purpose and Application-Based Systems |
| title | Anti-steganalysis for image on convolutional neural networks |
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