Privacy-Preserving Image Inpainting Using Markov Random Field Modeling
Ping Hao Kong, An Li, Daidou Guo, Liang Zhou, Chuan Qin, Xinpeng Zhang · IEEE Transactions on Multimedia · 2025
Cloud services have attracted extensive attention due to low cost, agility and mobility. However, when processing data on cloud servers, users may worry about semi-honest third parties stealing private information from them, hence, data encryption is applied for privacy protection. Inpainting is a technique that reconstructs certain undesirable regions in an image through an imperceptible manner, which can be accomplished by searching for well-matching candidate patches and copying them to to-be-inpainted locations. However, when the image is encrypted, the matched candidate patch searching is a challenging dilemma. Therefore, tackling these data-privacy issues for image inpainting over a cloud infrastructure, we propose an image inpainting scheme using Markov random field (MRF) modeling in encrypted domain. In this scheme, the sender encrypts the to-be-inapinted image by using a homomorphic cryptosystem that supports homomorphic ciphertext comparison. Then, the cloud realizes the MRF-based inpainting for encrypted images through some specific homomorphic operations. In addition, secure context descriptors are utilized to improve the inpainting of textures and structures. Finally, the receiver obtains the inpainted result through image decryption. The proposed scheme is proved to be secure through various cryptographic attacks. Qualitative and quantitative results demonstrate our scheme achieves better inpainted results in structure compared with state-of-the-art schemes in encrypted domain.