Recent Advances of Image Steganography With Generative Adversarial Networks
In the past few years, the Generative Adversarial Network (GAN), which proposed in 2014, has achieved great success. There have been increasing research achievements based on GAN in the field of computer vision and natural language processing. Image steganography is an information security technique...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.60575-60597 |
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| creator | Liu, Jia Ke, Yan Zhang, Zhuo Lei, Yu Li, Jun Zhang, Minqing Yang, Xiaoyuan |
| description | In the past few years, the Generative Adversarial Network (GAN), which proposed in 2014, has achieved great success. There have been increasing research achievements based on GAN in the field of computer vision and natural language processing. Image steganography is an information security technique aiming at hiding secret messages in common digital images for covert communication. Recently, research on image steganography has demonstrated great potential by introducing GAN and other neural network techniques. In this paper, we review the art of steganography with GANs according to the different strategies in data hiding, which are cover modification, cover selection, and cover synthesis. We discuss the characteristics of the three strategies of GAN-based steganography and analyze their evaluation metrics. Finally, some existing problems of image steganography with GAN are summarized and discussed. Potential future research topics are also forecasted. |
| doi_str_mv | 10.1109/ACCESS.2020.2983175 |
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| subjects | Computational modeling Computer vision cover synthesis Cryptography Digital imaging Gallium nitride generative adversarial nets Generative adversarial networks generative model Graphics Image steganography Measurement Natural language processing Neural networks Steganography |
| title | Recent Advances of Image Steganography With Generative Adversarial Networks |
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