Name
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\n $$^2$$\n CMH). This method consists of a deep cross-modal hashing model termed DACMH, and a secure index structure called CMH\n $$^2$$\n -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\n $$^2$$\n CMH outperforms the state-of-the-arts. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/S11063-021-10447-4 | Neural Processing Letters |
DocType | Volume | Citations |
Journal | 54 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Zhu | 1 | 854 | 51.69 |
| Jiayu Song | 2 | 0 | 0.34 |
| Zhan Yang | 3 | 0 | 0.34 |
| Wenti Huang | 4 | 3 | 1.38 |
| Chengyuan Zhang | 5 | 0 | 0.34 |
| Weiren Yu | 6 | 0 | 0.34 |