  In this paper, we introduce a preview of the Deepfakes Detection Challenge
(DFDC) dataset consisting of 5K videos featuring two facial modification
algorithms. A data collection campaign has been carried out where participating
actors have entered into an agreement to the use and manipulation of their
likenesses in our creation of the dataset. Diversity in several axes (gender,
skin-tone, age, etc.) has been considered and actors recorded videos with
arbitrary backgrounds thus bringing visual variability. Finally, a set of
specific metrics to evaluate the performance have been defined and two existing
models for detecting deepfakes have been tested to provide a reference
performance baseline. The DFDC dataset preview can be downloaded at:
deepfakedetectionchallenge.ai
