Open Access
ARTICLE
High Visual Quality Image Steganography Based on Encoder-Decoder Model
Yan Wang*, Zhangjie Fu, Xingming Sun
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
* Corresponding Author: Yan Wang. Email:
Journal of Cyber Security 2020, 2(3), 115-121. https://doi.org/10.32604/jcs.2020.012275
Received 23 June 2020; Accepted 21 July 2020; Issue published 14 September 2020
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.
Keywords
Cite This Article
Y. Wang, Z. Fu and X. Sun, "High visual quality image steganography based on encoder-decoder model,"
Journal of Cyber Security, vol. 2, no.3, pp. 115–121, 2020. https://doi.org/10.32604/jcs.2020.012275