Crypt-OR: A privacy-preserving distributed cloud computing framework for object-removal in the encrypted images

Abstract

Image inpainting is a technique to modify an image by removing/fill-up the undesired region(s) in a visually plausible manner. With the advancement of cloud applications, the cloud service providers (CSPs) provide image modifying services such as inpainting and undesired object removal to their users. However, these inpainting services require the user’s original image containing sensitive information that an adversary can misuse. In this paper, we address these privacy concerns in image enhancement techniques by proposing a novel privacy-preserving distributed-cloud-based framework for object removal in the encrypted images, namely Crypt-OR. The unknown pixels intensity value, obtained by removing the specified object(S), is approximated by incorporating the merits of the exemplar-based search–copy–paste approach and diffusion equation on the Shamir’s secret shares. The qualitative and quantitative analysis of Crypt-OR is evaluated over different real-world images with varying shaped and sized objects in the encrypted domain (ED). Crypt-OR outperforms the traditional exemplar-based object-removal schemes and is comparable with generative network-based inpainting schemes in the plain domain (PD). Further, Crypt-OR is proved information-theoretically secure in probabilistic and entropy viewpoints with standard cryptographic adversary attacks.