DAP$$^2$$CMH: Deep Adversarial Privacy-Preserving Cross-Modal Hashing

Autor: Zhan Yang, Chengyuan Zhang, Jiayu Song, Lei Zhu, Wenti Huang, Weiren Yu
Rok vydání: 2021
Předmět:
Zdroj: Neural Processing Letters. 54:2549-2569
ISSN: 1573-773X
1370-4621
DOI: 10.1007/s11063-021-10447-4
Popis: 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.
Databáze: OpenAIRE
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