Abstract:

Reconstructing the missing regions of an image is a typical requirement in computer vision. With deep inpainting algorithms, one can generate realistic inpainted images at a very low cost. However, such a powerful tool has potentially illegal or unethical uses, such as removing specific objects from images to deceive the public. Although many forensic methods for image inpainting have been proposed, their detection capabilities are still limited in complex inpainted images. Motivated by that, this paper proposed an efficient network based on dense connectivity to locate tampered regions in a realistic deep inpainting image. The network was an encoder-decoder architecture based on dense connectivity, where the introduced dense connected module can better capture subtle manipulation traces in realistic inpainted images. Furthermore, embedding the Ghost modules, dilated convolutions, and the channel attention mechanism in dense connected blocks could achieve better localization performance.Experiments demonstrate that the proposed method can effectively locate the inpainted regions in sophisticated deep inpainting images, and also show that the method fulfilling the robustness requirements of JPEG compression and rotation.

Key words: deep inpainting, forgery detection, dense connectivity, Ghost module