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Coverless real-time image information hiding based on image block matching and dense convolutional network

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成果类型:
期刊论文
作者:
Luo, Yuanjing;Qin, Jiaohua*;Xiang, Xuyu*;Tan, Yun;Liu, Qiang;...
通讯作者:
Qin, Jiaohua;Xiang, Xuyu
作者机构:
[Qin, Jiaohua; Xiang, Xuyu; Liu, Qiang; Tan, Yun; Xiang, XY; Luo, Yuanjing] Cent South Univ Forestry & Technol, Coll Comp Sci & Informat Technol, Changsha, Peoples R China.
[Xiang, Xuyu] Univ Alabama, Coll Commun & Informat Sci, Tuscaloosa, AL 35487 USA.
[Xiang, Lingyun] Changsha Univ Sci & Technol, Sch Comp & Sci Engn, Changsha, Peoples R China.
通讯机构:
[Qin, JH; Xiang, XY] C
[Xiang, Xuyu] U
Cent South Univ Forestry & Technol, Coll Comp Sci & Informat Technol, Changsha, Peoples R China.
Univ Alabama, Coll Commun & Informat Sci, Tuscaloosa, AL 35487 USA.
语种:
英文
关键词:
Coverless information hiding;Data hiding;Deep learning;DCT;DenseNet;Real-time image processing
期刊:
Journal of Real-Time Image Processing
ISSN:
1861-8200
年:
2020
卷:
17
期:
1
页码:
125-135
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
Information security has become a key issue of public concern recently. In order to radically resist the decryption and analysis in the field of image information hiding and significantly improve the security of the secret information, a novel coverless information hiding approach based on deep learning is proposed in this paper. Deep learning can select the appropriate carrier according to requirements to achieve real-time image data hiding and the high-level semantic features extracted by CNN are more accurate than the low-level features. This method does not need to employ the designated im...

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