Anti-forensics of diffusion-based image inpainting

Abstract

Diffusion-based inpainting can be used to repair some damaged parts or remove the undesirable regions in an image. Generally, good visual effects can be achieved after inpainting. However, some traces, such as the differences of local variances and noise pattern, are left in the inpainted image, making it easy for the forensic algorithms to locate the inpainted regions. To eliminate this drawback and achieve an anti-forensics capability, we propose an approach that can remove the traces of the diffusion-based inpainting. Since the pixel values of the inpainted regions are diffused inward by the surrounding pixels, we first analyze the noise pattern of the pixels neighboring the inpainted regions and select the nearby pixels that are directly used for inpainting. After that, a statistical probability model is constructed for each channel in the image, which is used to generate the noise pattern and fill the inpainted regions. Experimental results show that the proposed approach has a good capability for anti-forensics.