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Privacy-preserving inpainting for outsourced image
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Cao, Fang, Sun, Jiayi, Luo, Xiangyang, Qin, Chuan and Chang, Ching-Chun (2021) Privacy-preserving inpainting for outsourced image. International Journal of Distributed Sensor Networks, 17 (11). doi:10.1177/15501477211059092 ISSN 1550-1329.
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Official URL: https://doi.org/10.1177/15501477211059092
Abstract
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.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Image reconstruction, Image processing -- Digital techniques, Cloud computing -- Data processing, Cloud computing -- Security measures | |||||||||
| Journal or Publication Title: | International Journal of Distributed Sensor Networks | |||||||||
| Publisher: | Hindawi Publishing Corporation | |||||||||
| ISSN: | 1550-1329 | |||||||||
| Official Date: | 29 November 2021 | |||||||||
| Dates: |
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| Volume: | 17 | |||||||||
| Number: | 11 | |||||||||
| DOI: | 10.1177/15501477211059092 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 21 December 2021 | |||||||||
| Date of first compliant Open Access: | 4 January 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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