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PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting | IEEE Conference Publication | IEEE Xplore

PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting


Abstract:

The cloud-based multimedia service becomes increasingly popular in the last decade, however, it poses a serious threat to the client’s privacy. To address this issue, man...Show More

Abstract:

The cloud-based multimedia service becomes increasingly popular in the last decade, however, it poses a serious threat to the client’s privacy. To address this issue, many methods utilized image encryption as a defense mechanism. However, the encrypted images look quite different from the natural images, making them vulnerable to attackers. In this paper, we propose a novel method namely PIRNet, which operates privacy-preserving image restoration in the steganographic domain. Compared to existing methods, our method offers significant advantages in terms of invisibility and security. Specifically, we first propose a wavelet Lifting-based Invertible Hiding (LIH) network to conceal the secret image into the stego image. Then, a Lifting-based Secure Restoration (LSR) network is utilized to perform image restoration in the steganographic domain. Since the secret image remains hidden throughout the whole image restoration process, the privacy of clients can be largely ensured. In addition, since the stego image looks visually the same as the cover image, the attackers can hardly discover it, which significantly improves the security. The experimental results on different datasets show the superiority of our PIRNet over the existing methods on various privacy-preserving image restoration tasks, including image denoising, deblurring and super-resolution.
Date of Conference: 01-06 October 2023
Date Added to IEEE Xplore: 15 January 2024
ISBN Information:

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Conference Location: Paris, France

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References is not available for this document.

1. Introduction

Recently, the cloud-based multimedia services have developed rapidly with the fast growing cloud computing technology. The Software-as-a-Service (SaaS) [5] such as Amazon Web Service (AWS) [1] and Google Cloud Platform (GCP) [2] provides strong computing resource to the clients, which allows them to perform efficient image processing online. However, image processing in the cloud poses a serious threat to the client’s privacy. A hacker or a malicious service provider can easily access the clients’ private photos, and discover their personal identity, social connections, and visited places for unauthorized uses.

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References

References is not available for this document.