HidingGAN: High Capacity Information Hiding with Generative Adversarial Network
Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problem...
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| Veröffentlicht in: | Computer graphics forum Jg. 38; H. 7; S. 393 - 401 |
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Blackwell Publishing Ltd
01.10.2019
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception‐ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high‐dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits‐per‐pixel (bpp) at 256 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels. |
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| AbstractList | Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception‐ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high‐dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits‐per‐pixel (bpp) at 256 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels. |
| Author | Gao, Neng Zha, Daren Wang, Xin Li, Linghui Wang, Zihan Xiang, Ji |
| Author_xml | – sequence: 1 givenname: Zihan surname: Wang fullname: Wang, Zihan organization: Chinese Academy of Sciences – sequence: 2 givenname: Neng surname: Gao fullname: Gao, Neng organization: Chinese Academy of Sciences – sequence: 3 givenname: Xin surname: Wang fullname: Wang, Xin email: wangxin@iie.ac.cn organization: Chinese Academy of Sciences – sequence: 4 givenname: Ji surname: Xiang fullname: Xiang, Ji organization: Chinese Academy of Sciences – sequence: 5 givenname: Daren surname: Zha fullname: Zha, Daren organization: Chinese Academy of Sciences – sequence: 6 givenname: Linghui surname: Li fullname: Li, Linghui organization: Chinese Academy of Sciences |
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| Cites_doi | 10.1007/978-3-319-10602-1_48 10.23915/distill.00003 10.18653/v1/P17-3017 10.1109/TIFS.2017.2710946 10.1109/WIFS.2012.6412655 10.1109/CVPR.2015.7298594 10.1007/978-3-030-04179-3_22 10.1109/CVPR.2016.90 10.1109/ICCV.2015.425 10.1016/j.engappai.2015.12.013 10.1186/1687-417X-2014-1 10.1609/aaai.v31i1.11231 10.1109/LSP.2017.2745572 10.1007/978-3-319-46475-6_43 10.1007/978-3-319-77380-3_51 10.1007/978-3-642-16435-4_13 |
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| Snippet | Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from... |
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| SubjectTerms | Algorithms Artificial neural networks CCS Concepts Computing methodologies → Computer vision tasks Generative adversarial networks Machine learning Pixels Security and privacy → Privacy protections Steganography |
| Title | HidingGAN: High Capacity Information Hiding with Generative Adversarial Network |
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