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DAP2CMH : deep adversarial privacy-preserving cross-modal hashing
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Zhu, Lei, Song, Jiayu, Yang, Zhan, Huang, Wenti, Zhang, Chengyuan and Yu, Weiren (2022) DAP2CMH : deep adversarial privacy-preserving cross-modal hashing. Neural Processing Letters, 54 . pp. 2549-2569. doi:10.1007/s11063-021-10447-4 ISSN 1370-4621.
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Official URL: http://dx.doi.org/10.1007/s11063-021-10447-4
Abstract
Privacy-preserving cross-modal retrieval is a significant problem in the area of multimedia analysis. As the amount of data is exploding, cross-modal data analysis and retrieval is often realized on cloud computing environment. Therefore, the privacy protection of large-scale cross-modal data has become a problem that can not be ignored. To further improve the accuracy and efficiency of privacy-preserving search, this paper proposes a novel cross-modal hashing scheme, named deep adversarial privacy-preserving cross-modal hashing (DAP2CMH). This method consists of a deep cross-modal hashing model termed DACMH, and a secure index structure called CMH2-Tree. The former is a combination of deep hashing and adversarial learning to capture intra-modal and inter-modal correlation. The latter is a hierarchical hashing index structure that can provide efficient data organization based on cross-modal hash codes. We conduct comprehensive experiments on three common used benchmarks. The results show that the proposed approach DAP2CMH outperforms the state-of-the-arts.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Journal or Publication Title: | Neural Processing Letters | ||||||||
Publisher: | Springer New York LLC | ||||||||
ISSN: | 1370-4621 | ||||||||
Official Date: | August 2022 | ||||||||
Dates: |
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Volume: | 54 | ||||||||
Page Range: | pp. 2549-2569 | ||||||||
DOI: | 10.1007/s11063-021-10447-4 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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