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DHU-Net: High-capacity binary data hiding network based on improved U-Net

Authors
Duan, XintaoLi, ChunWei, BingxinWu, GuomingQin, ChuanNam, Haewoon
Issue Date
Apr-2024
Publisher
Elsevier B.V.
Keywords
Binary data hiding; DHU-Net; Flow encoding; Losslessly
Citation
Neurocomputing, v.576, pp 1 - 16
Pages
16
Indexed
SCIE
SCOPUS
Journal Title
Neurocomputing
Volume
576
Start Page
1
End Page
16
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118317
DOI
10.1016/j.neucom.2024.127314
ISSN
0925-2312
1872-8286
Abstract
Due to limited data-hiding capacity and low extraction accuracy, most existing data hiding schemes have difficulty in high capacity hiding and lossless extraction of binary data. This paper proposes a novel binary data hiding network (DHU-Net) based on improved U-Net and flow encoding, which deepens the network structure and facilitates lossless extraction of high-capacity binary data. The BN layer is added to address overfitting and prevent vanishing gradient or exploding gradient. Furthermore, a novel binary-to-spatial domain mapping method is proposed to map the binary data to the spatial domain. Experimental results show that our scheme can achieve lossless extraction of binary data while maintaining low distortion and sufficient security. DHU-Net provides a promising solution for secure and efficient transmission of binary data through data hiding. © 2024 Elsevier B.V.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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