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

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 lear...

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Vydáno v:Journal of real-time image processing Ročník 17; číslo 1; s. 125 - 135
Hlavní autoři: Luo, Yuanjing, Qin, Jiaohua, Xiang, Xuyu, Tan, Yun, Liu, Qiang, Xiang, Lingyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020
Springer Nature B.V
Témata:
Accuracy
Computer Graphics
Computer Science
Data compression
Datasets
Deep learning
Image databases
Image Processing and Computer Vision
Matching
Multimedia Information Systems
Neural networks
Pattern Recognition
Performance evaluation
Real time
Retrieval
Security
Semantics
Signal processing
Signal,Image and Speech Processing
Special Issue Paper
Stitching
Deep learning
DenseNet
Data hiding
Coverless information hiding
Real-time image processing
DCT
ISSN:1861-8200, 1861-8219
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Shrnutí: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 image for embedding the secret data but transfer a set of real-time stego-images which share one or several visually similar blocks with the given secret image. In this approach, a group of real-time images searched online are segmented according to specific requirements. Then, the DenseNet is used to extract the high-level semantic features of each similar block. At the same time, a robust hash sequence with feature sequence, DC and location is generated by DCT. The inverted index structure based on the hash sequence is constructed to attain real-time image matching efficiently. At the sending end, the stego-images are matched and sent through feature retrieval. At the receiving end, the secret image can be recovered by extracting similar blocks through the received stego-images and stitching the image blocks according to the location information. Experimental results demonstrate that the proposed method without any modification traces provides better robustness and has higher retrieval accuracy and capacity when compared with some existing coverless image information hiding.
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ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-019-00917-3