Yuanjing Luo1, Jiaohua Qin1, *, Xuyu Xiang1, Yun Tan1, Zhibin He1, Neal N. Xiong2
CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1281-1295, 2020, DOI:10.32604/cmc.2020.010867
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… More >
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