DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance

Abstract

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the \(\text {1}^{\text {st}}\) generation such as UADFV and FaceForensics++ up to the latest databases of the \(\text {2}^{\text {nd}}\) generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both \(\text {1}^{\text {st}}\) and \(\text {2}^{\text {nd}}\) DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specific facial regions as input to the fake detection system.