Privacy-preserving inpainting for outsourced image

Fang Cao,Ching-Chun Chang,Xiangyang Luo,Chuan Qin,Jiayi Sun

doi: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.

Highlights

Image inpainting is known as image retouching, the idea of which is inherited from ancient technique of manually repairing valuable artworks in an indiscernible way.[1]
We mainly focus on the non-learningbased image inpainting methods, which have efficient and clear algorithms to follow and are more possible to be implemented in the encrypted domain with privacypreserving capability than the learning-based methods
We propose a new privacy-preserving inpainting framework for outsource image and a specific encrypted-image inpainting scheme, which can repair scratches and remove undesirable objects in images seamlessly and can protect the user privacy through encrypting image contents toward image restorer

Summary

Introduction

Image inpainting is known as image retouching, the idea of which is inherited from ancient technique of manually repairing valuable artworks in an indiscernible way.[1]. After conducting decryption for the received data from cloud server, the user can obtain the processed data with desirable effects in the plaintext domain Based on this application scenario and requirements, in recent years, the studies of secure signal processing in encrypted domain have been widely investigated, such as transform,[27] compression,[28] denoising,[29] feature extraction,[30] and data hiding[31,32] for encrypted data. Experimental results and analysis are presented in section ‘‘Experimental results and analysis.’’ Section ‘‘Conclusion’’ concludes the article

Related works

Experimental results and analysis

Conclusion

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