PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting

Xin Deng, Chao Gao, Mai Xu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 22368-22377

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.