Invisible steganography via generative adversarial networks
Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learni...
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| Published in | Multimedia tools and applications Vol. 78; no. 7; pp. 8559 - 8575 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.04.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-018-6951-z |
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| Abstract | Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learning to do image steganography, but these works still have problems in capacity, invisibility and security. In this paper, we propose a novel CNN architecture named as ISGAN to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side. There are three contributions in our work: (i) we improve the invisibility by hiding the secret image only in the Y channel of the cover image; (ii) We introduce the generative adversarial networks to strengthen the security by minimizing the divergence between the empirical probability distributions of stego images and natural images. (iii) In order to associate with the human visual system better, we construct a mixed loss function which is more appropriate for steganography to generate more realistic stego images and reveal out more better secret images. Experiment results show that ISGAN can achieve start-of-art performances on LFW, PASCAL-VOC12 and ImageNet datasets. |
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| AbstractList | Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learning to do image steganography, but these works still have problems in capacity, invisibility and security. In this paper, we propose a novel CNN architecture named as ISGAN to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side. There are three contributions in our work: (i) we improve the invisibility by hiding the secret image only in the Y channel of the cover image; (ii) We introduce the generative adversarial networks to strengthen the security by minimizing the divergence between the empirical probability distributions of stego images and natural images. (iii) In order to associate with the human visual system better, we construct a mixed loss function which is more appropriate for steganography to generate more realistic stego images and reveal out more better secret images. Experiment results show that ISGAN can achieve start-of-art performances on LFW, PASCAL-VOC12 and ImageNet datasets. |
| Author | Dong, Shiqi Zhang, Ru Liu, Jianyi |
| Author_xml | – sequence: 1 givenname: Ru surname: Zhang fullname: Zhang, Ru organization: School of Cyberspace Security, Beijing University of Posts and Telecommunications – sequence: 2 givenname: Shiqi surname: Dong fullname: Dong, Shiqi email: shiqidong@bupt.edu.cn organization: School of Cyberspace Security, Beijing University of Posts and Telecommunications – sequence: 3 givenname: Jianyi surname: Liu fullname: Liu, Jianyi organization: School of Cyberspace Security, Beijing University of Posts and Telecommunications |
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| SubjectTerms | Artificial neural networks Computer Communication Networks Computer Science Data Structures and Information Theory Deep learning Divergence Generative adversarial networks Layouts Machine learning Multimedia Information Systems Special Purpose and Application-Based Systems Steganography Visibility |
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| Title | Invisible steganography via generative adversarial networks |
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