  Fake content has grown at an incredible rate over the past few years. The
spread of social media and online platforms makes their dissemination on a
large scale increasingly accessible by malicious actors. In parallel, due to
the growing diffusion of fake image generation methods, many Deep
Learning-based detection techniques have been proposed. Most of those methods
rely on extracting salient features from RGB images to detect through a binary
classifier if the image is fake or real. In this paper, we proposed DepthFake,
a study on how to improve classical RGB-based approaches with depth-maps. The
depth information is extracted from RGB images with recent monocular depth
estimation techniques. Here, we demonstrate the effective contribution of
depth-maps to the deepfake detection task on robust pre-trained architectures.
The proposed RGBD approach is in fact able to achieve an average improvement of
3.20% and up to 11.7% for some deepfake attacks with respect to standard RGB
architectures over the FaceForensic++ dataset.
