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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.


用于 PC 流媒体或视频通话的实时面部交换。您可以使用经过训练的面部模型交换网络摄像头中的面部或视频中的面部。

Deepfake防御不仅需要检测方面的研究,还需要生成方法的努力。然而,当前的深度伪造方法受到工作流程模糊和性能不佳的影响。为了解决这个问题,我们提出了 DeepFaceLab,这是当前占主导地位的换脸深度换脸框架。它提供了必要的工具以及易于使用的方式来进行高质量的换脸。它还为需要使用其他功能增强管道而无需编写复杂的样板代码的人们提供了灵活且松散的耦合结构。

我们详细介绍了 DeepFaceLab 实施的原理,并介绍了其管道,通过该管道,用户可以轻松地修改管道的各个方面,以实现其定制目的。值得注意的是,DeepFaceLab 可以实现高保真度的影院级效果。我们通过将我们的方法与其他换脸方法进行比较来展示我们系统的优势。

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