The low cost, agility, and mobility of cloud services for processing and storage data have attracted user’s attention today. Untrusted third parties support these services, and users are always concerned about utilizing them for personal data. Addressing these data-privacy issues for image inpainting over the cloud infrastructure(s), we propose a model, 2DInpaint, to perform image inpainting by protecting image information from an eavesdropping adversary. Inpainting is a technique for modifying an image in an undetectable manner with applications ranging from restoration of damaged photographs to object-removal and replacement of lost blocks in image coding and transmission. It can be accomplished by propagating the information in the isophotes direction of the desired region(s) from the neighborhood. Performing this propagation when the image is in the encrypted domain (ED) is a challenging dilemma. The challenge is addressed by employing a modified version of 2D-bicubic interpolation over the region to be inpainted in ED. The ramp secret sharing scheme is utilized to secure image information and to reduce storage overhead over the cloud server. 2DInpaint is proved to be information-theoretical secure in a probabilistic viewpoint and through various cryptographic attacks. The qualitative and quantitative results of 2DInpaint are analyzed under the scenarios of classical image inpainting, object-removal, and text-removal, and compared with the schemes in the plain domain. Moreover, no limitations related to the topology of the region to be inpainted are required using our approach. To the best of our knowledge, 2DInpaint is the first move towards image inpainting in the ED.

今天，用于处理和存储数据的云服务的低成本，敏捷性和移动性吸引了用户的注意。不受信任的第三方支持这些服务，用户始终担心将其用于个人数据。为解决通过云基础架构进行图像修复的这些数据隐私问题，我们提出了2DInpaint模型，通过保护图像信息免受窃听对手的攻击来执行图像修复。修复是一种以不可检测的方式修改图像的技术，其应用范围包括从受损照片的恢复到物体的去除以及图像编码和传输中丢失块的替换。可以通过在邻近区域的所需区域的等渗线方向上传播信息来实现。当图像位于加密域（ED）中时执行此传播是一个难题。通过在ED中要修复的区域上使用二维双三次插值的修改版本，可以解决该挑战。斜坡秘密共享方案用于保护图像信息并减少云服务器上的存储开销。2D画图从概率的角度和通过各种加密攻击证明，该方法在信息理论上是安全的。在经典图像修复，对象去除和文本去除的场景下，对2DInpaint的定性和定量结果进行了分析，并与普通方案进行了比较。而且，使用我们的方法不需要与要修补区域的拓扑相关的限制。据我们所知，2DInpaint是ED中走向图像修复的第一步。