  Deepfake defense not only requires the research of detection but also
requires the efforts of generation methods. However, current deepfake methods
suffer the effects of obscure workflow and poor performance. To solve this
problem, we present DeepFaceLab, the current dominant deepfake framework for
face-swapping. It provides the necessary tools as well as an easy-to-use way to
conduct high-quality face-swapping. It also offers a flexible and loose
coupling structure for people who need to strengthen their pipeline with other
features without writing complicated boilerplate code. We detail the principles
that drive the implementation of DeepFaceLab and introduce its pipeline,
through which every aspect of the pipeline can be modified painlessly by users
to achieve their customization purpose. It is noteworthy that DeepFaceLab could
achieve cinema-quality results with high fidelity. We demonstrate the advantage
of our system by comparing our approach with other face-swapping methods.For
more information, please visit:https://github.com/iperov/DeepFaceLab/.
