Privacy-preserving inpainting for outsourced image
In this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content own...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2021-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211059092 |
| _version_ | 1797727149449281536 |
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| author | Fang Cao Jiayi Sun Xiangyang Luo Chuan Qin Ching-Chun Chang |
| author_facet | Fang Cao Jiayi Sun Xiangyang Luo Chuan Qin Ching-Chun Chang |
| author_sort | Fang Cao |
| collection | DOAJ |
| description | In this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content owner first encrypts his or her damaged image for privacy protection and outsources the encrypted, damaged image to image restorer, who may be a cloud server with powerful computation capability. Image restorer performs inpainting in encrypted domain and sends the inpainted and encrypted image back to content owner or authorized receiver, who can acquire final inpainted result in plaintext domain through decryption. In our encrypted-image inpainting scheme, with the assist of Johnson–Lindenstrauss transform that can preserve Euclidean distance between two vectors before and after encryption, the best-matching block with the smallest distance to current block can be found and utilized for patch filling in Paillier-encrypted image. To eliminate mosaic effect after decryption, weighted mean filtering in encrypted domain is conducted with Paillier homomorphic properties. Experimental results show that our privacy-preserving inpainting framework can be effectively applied in secure cloud computing, and the proposed encrypted-image inpainting scheme achieves comparable visual quality of inpainted results with some typical inpainting schemes in plaintext domain. |
| first_indexed | 2024-03-12T10:55:45Z |
| format | Article |
| id | doaj.art-a4020df0be7d4b369b05e5c08d161bfa |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T10:55:45Z |
| publishDate | 2021-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-a4020df0be7d4b369b05e5c08d161bfa2023-09-02T06:24:02ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772021-11-011710.1177/15501477211059092Privacy-preserving inpainting for outsourced imageFang Cao0Jiayi Sun1Xiangyang Luo2Chuan Qin3Ching-Chun Chang4Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, ChinaSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaZhengzhou Information Science and Technology Institute, Zhengzhou, ChinaSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Computer Science, University of Warwick, Coventry, UKIn this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content owner first encrypts his or her damaged image for privacy protection and outsources the encrypted, damaged image to image restorer, who may be a cloud server with powerful computation capability. Image restorer performs inpainting in encrypted domain and sends the inpainted and encrypted image back to content owner or authorized receiver, who can acquire final inpainted result in plaintext domain through decryption. In our encrypted-image inpainting scheme, with the assist of Johnson–Lindenstrauss transform that can preserve Euclidean distance between two vectors before and after encryption, the best-matching block with the smallest distance to current block can be found and utilized for patch filling in Paillier-encrypted image. To eliminate mosaic effect after decryption, weighted mean filtering in encrypted domain is conducted with Paillier homomorphic properties. Experimental results show that our privacy-preserving inpainting framework can be effectively applied in secure cloud computing, and the proposed encrypted-image inpainting scheme achieves comparable visual quality of inpainted results with some typical inpainting schemes in plaintext domain.https://doi.org/10.1177/15501477211059092 |
| spellingShingle | Fang Cao Jiayi Sun Xiangyang Luo Chuan Qin Ching-Chun Chang Privacy-preserving inpainting for outsourced image International Journal of Distributed Sensor Networks |
| title | Privacy-preserving inpainting for outsourced image |
| title_full | Privacy-preserving inpainting for outsourced image |
| title_fullStr | Privacy-preserving inpainting for outsourced image |
| title_full_unstemmed | Privacy-preserving inpainting for outsourced image |
| title_short | Privacy-preserving inpainting for outsourced image |
| title_sort | privacy preserving inpainting for outsourced image |
| url | https://doi.org/10.1177/15501477211059092 |
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