High Visual Quality Image Steganography Based on Encoder-Decoder Model

作     者：Yan Wang Zhangjie Fu Xingming Sun 

作者机构：School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina 

出 版 物：《Journal of Cyber Security》 (网络安全杂志（英文）)

年 卷 期：2020年第2卷第3期

页      面：115-121页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by the National Natural Science Foundation of China under Grant Nos.U1836110 U1836208 

主　　题：Steganaography visual quality cyber security 

摘      要：Nowadays,with the popularization of network technology,more and more people are concerned about the problem of cyber ***,a technique dedicated to protecting peoples’private data,has become a hot topic in the research ***,there are still some problems in the current *** 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 *** this paper,we propose a novel high visual quality image steganographic neural network based on encoder-decoder model to solve these problems mentioned ***,we design a novel encoder module by applying the structure of U-Net++,which aims to achieve higher visual ***,the steganalyzer is heuristically added into the model in order to improve the ***,the network model is used to generate the stego images via adversarial *** results demonstrate that our proposed scheme can achieve better performance in terms of visual quality and security.