  Deepfakes are a recent off-the-shelf manipulation technique that allows
anyone to swap two identities in a single video. In addition to Deepfakes, a
variety of GAN-based face swapping methods have also been published with
accompanying code. To counter this emerging threat, we have constructed an
extremely large face swap video dataset to enable the training of detection
models, and organized the accompanying DeepFake Detection Challenge (DFDC)
Kaggle competition. Importantly, all recorded subjects agreed to participate in
and have their likenesses modified during the construction of the face-swapped
dataset. The DFDC dataset is by far the largest currently and publicly
available face swap video dataset, with over 100,000 total clips sourced from
3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned
methods. In addition to describing the methods used to construct the dataset,
we provide a detailed analysis of the top submissions from the Kaggle contest.
We show although Deepfake detection is extremely difficult and still an
unsolved problem, a Deepfake detection model trained only on the DFDC can
generalize to real "in-the-wild" Deepfake videos, and such a model can be a
valuable analysis tool when analyzing potentially Deepfaked videos. Training,
validation and testing corpuses can be downloaded from
https://ai.facebook.com/datasets/dfdc.

Computer Vision and Pattern Recognition;Machine Learning