Open Access
ARTICLE
Coverless Image Steganography Based on Image Segmentation
Yuanjing Luo1, Jiaohua Qin1, *, Xuyu Xiang1, Yun Tan1, Zhibin He1, Neal N. Xiong2
1 College of Computer Science and Information Technology, Central South University of Forestry &
Technology, Changsha, 410114, China.
2 Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, 74464, USA.
* Corresponding Author: Jiaohua Qin. Email: .
Computers, Materials & Continua 2020, 64(2), 1281-1295. https://doi.org/10.32604/cmc.2020.010867
Received 02 April 2020; Accepted 17 April 2020; Issue published 10 June 2020
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.
Keywords
Cite This Article
Y. Luo, J. Qin, X. Xiang, Y. Tan, Z. He
et al., "Coverless image steganography based on image segmentation,"
Computers, Materials & Continua, vol. 64, no.2, pp. 1281–1295, 2020. https://doi.org/10.32604/cmc.2020.010867
Citations