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A Survey of Image Information Hiding Algorithms Based on Deep Learning

Ruohan Meng1,2,*, Qi Cui1,2, Chengsheng Yuan1,2,3

School of Computer and Software, Nanjing University of Information Science and Technology, Ning Liu Road, No. 219, Nanjing, 210044, China.
Jiangsu Engineering Centre of Network Monitoring, Ning Liu Road, No. 219, Nanjing, 210044, China .
Department of Electrical and computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 374, Canada.

*Corresponding Author: Ruohan Meng. Email: email.

Computer Modeling in Engineering & Sciences 2018, 117(3), 425-454.


With the development of data science and technology, information security has been further concerned. In order to solve privacy problems such as personal privacy being peeped and copyright being infringed, information hiding algorithms has been developed. Image information hiding is to make use of the redundancy of the cover image to hide secret information in it. Ensuring that the stego image cannot be distinguished from the cover image, and sending secret information to receiver through the transmission of the stego image. At present, the model based on deep learning is also widely applied to the field of information hiding. This paper makes an overall conclusion on image information hiding based on deep learning. It is divided into four parts of steganography algorithms, watermarking embedding algorithms, coverless information hiding algorithms and steganalysis algorithms based on deep learning. From these four aspects, the state-of-the-art information hiding technologies based on deep learning are illustrated and analyzed.


Cite This Article

Meng, R., Cui, Q., Yuan, C. (2018). A Survey of Image Information Hiding Algorithms Based on Deep Learning. CMES-Computer Modeling in Engineering & Sciences, 117(3), 425–454.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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