@Article{cmc.2020.010867,
AUTHOR = {Yuanjing Luo, Jiaohua Qin, Xuyu Xiang, Yun Tan, Zhibin He, Neal N. Xiong},
TITLE = {Coverless Image Steganography Based on Image Segmentation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1281--1295},
URL = {http://www.techscience.com/cmc/v64n2/39360},
ISSN = {1546-2226},
ABSTRACT = {To resist the risk of the stego-image being maliciously altered during 
transmission, we propose a coverless image steganography method based on image 
segmentation. Most existing coverless steganography methods are based on whole feature 
mapping, which has poor robustness when facing geometric attacks, because the contents
in the image are easy to lost. To solve this problem, we use ResNet to extract semantic 
features, and segment the object areas from the image through Mask RCNN for 
information hiding. These selected object areas have ethical structural integrity and are 
not located in the visual center of the image, reducing the information loss of malicious 
attacks. Then, these object areas will be binarized to generate hash sequences for 
information mapping. In transmission, only a set of stego-images unrelated to the secret
information are transmitted, so it can fundamentally resist steganalysis. At the same time, 
since both Mask RCNN and ResNet have excellent robustness, pre-training the model 
through supervised learning can achieve good performance. The robust hash algorithm 
can also resist attacks during transmission. Although image segmentation will reduce the 
capacity, multiple object areas can be extracted from an image to ensure the capacity to a 
certain extent. Experimental results show that compared with other coverless image 
steganography methods, our method is more robust when facing geometric attacks.},
DOI = {10.32604/cmc.2020.010867}
}