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...

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Autores principales:	Fang Cao, Jiayi Sun, Xiangyang Luo, Chuan Qin, Ching-Chun Chang
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/a4020df0be7d4b369b05e5c08d161bfa
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spelling	oai:doaj.org-article:a4020df0be7d4b369b05e5c08d161bfa2021-12-02T03:05:04ZPrivacy-preserving inpainting for outsourced image1550-147710.1177/15501477211059092https://doaj.org/article/a4020df0be7d4b369b05e5c08d161bfa2021-11-01T00:00:00Zhttps://doi.org/10.1177/15501477211059092https://doaj.org/toc/1550-1477In 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.Fang CaoJiayi SunXiangyang LuoChuan QinChing-Chun ChangSAGE PublishingarticleElectronic computers. Computer scienceQA75.5-76.95ENInternational Journal of Distributed Sensor Networks, Vol 17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Fang Cao Jiayi Sun Xiangyang Luo Chuan Qin Ching-Chun Chang Privacy-preserving inpainting for outsourced image
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.
format	article
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
title	Privacy-preserving inpainting for outsourced image
title_short	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_sort	privacy-preserving inpainting for outsourced image
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/a4020df0be7d4b369b05e5c08d161bfa
work_keys_str_mv	AT fangcao privacypreservinginpaintingforoutsourcedimage AT jiayisun privacypreservinginpaintingforoutsourcedimage AT xiangyangluo privacypreservinginpaintingforoutsourcedimage AT chuanqin privacypreservinginpaintingforoutsourcedimage AT chingchunchang privacypreservinginpaintingforoutsourcedimage
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Privacy-preserving inpainting for outsourced image

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