  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 1st generation such as UADFV and FaceForensics++ up to the
latest databases of the 2nd 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 1st and
2nd 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.
  Among all the findings resulting from our experiments, we highlight the poor
fake detection results achieved even by the strongest state-of-the-art fake
detectors in the latest DeepFake databases of the 2nd generation, with Equal
Error Rate results ranging from 15% to 30%. These results remark the necessity
of further research to develop more sophisticated fake detectors.
