@Article{jcs.2020.012275,
AUTHOR = {Yan Wang, Zhangjie Fu, Xingming Sun},
TITLE = {High Visual Quality Image Steganography Based on Encoder-Decoder Model},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {115--121},
URL = {http://www.techscience.com/JCS/v2n3/40139},
ISSN = {2579-0064},
ABSTRACT = { Nowadays, with the popularization of network technology, more and 
more people are concerned about the problem of cyber security. Steganography, 
a technique dedicated to protecting peoples’ private data, has become a hot topic 
in the research field. However, there are still some problems in the current 
research. For example, the visual quality of dense images generated by some 
steganographic algorithms is not good enough; the security of the steganographic
algorithm is not high enough, which makes it easy to be attacked by others. In 
this paper, we propose a novel high visual quality image steganographic neural 
network based on encoder-decoder model to solve these problems mentioned 
above. Firstly, we design a novel encoder module by applying the structure of UNet++, which aims to achieve higher visual quality. Then, the steganalyzer is 
heuristically added into the model in order to improve the security. Finally, the 
network model is used to generate the stego images via adversarial training.
Experimental results demonstrate that our proposed scheme can achieve better 
performance in terms of visual quality and security.},
DOI = {10.32604/jcs.2020.012275}
}