Localization of Diffusion-Based Inpainting in Digital Images

Image inpainting, an image processing technique for restoring missing or damaged image regions, can be utilized by forgers for removing objects in digital images. Since no obviously perceptible artifacts are left after inpainting, it is necessary to develop methods for detecting the presence of inpainting. In general, there are two main categories of image inpainting techniques: exemplar-based and diffusion-based techniques. Although several methods have been proposed for detecting exemplar-based inpainting, there is still no effective method for detecting diffusion-based inpainting. Usually, the tampered regions manipulated by diffusion-based inpainting techniques are much smaller than those manipulated by exemplar-based ones, presenting more challenges in detecting these regions. As a pioneering attempt, this paper proposes a method for the localization of diffusion-based inpainted regions in digital images. We first analyze the diffusion process in inpainting, and observe that the changes in the image Laplacian along the direction perpendicular to the gradient are different in the inpainted and untouched regions. Following this observation, we construct a feature set based on the intra-channel and inter-channel local variances of the changes to identify the inpainted regions. Finally, two effective post-processing operations are designed for further refining of the localization result. The extensive experimental results evaluated on both synthetic and realistic inpainted images show the effectiveness of the proposed method.