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Neural Processing Letters
Erschienen in: Neural Processing Letters 4/2022

07.03.2021

DAP\(^2\)CMH: Deep Adversarial Privacy-Preserving Cross-Modal Hashing

verfasst von: Lei Zhu, Jiayu Song, Zhan Yang, Wenti Huang, Chengyuan Zhang, Weiren Yu

Erschienen in: Neural Processing Letters | Ausgabe 4/2022

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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 (DAP\(^2\)CMH). This method consists of a deep cross-modal hashing model termed DACMH, and a secure index structure called CMH\(^2\)-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 DAP\(^2\)CMH outperforms the state-of-the-arts.
Vorheriger Artikel Self-Adaptive Clustering of Dynamic Multi-Graph Learning
Nächster Artikel A Robust Cost-Sensitive Feature Selection Via Self-Paced Learning Regularization

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Metadaten
Titel
DAPCMH: Deep Adversarial Privacy-Preserving Cross-Modal Hashing
verfasst von
Lei Zhu
Jiayu Song
Zhan Yang
Wenti Huang
Chengyuan Zhang
Weiren Yu
Publikationsdatum
07.03.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 4/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10447-4

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