Summary
In this chapter, we learned the workflow required to create a deepfake using the open source Faceswap software. The importance of data variety was discussed and the steps required to acquire, curate and generate face sets were demonstrated. We learned how to train a model within Faceswap, and how to gauge when a model has been fully trained, as well as learned some tricks to improve the quality of the model. Finally, we learned how to take our trained model and apply it to a source video to swap the faces within the video.
In the next chapter, we will begin to take a hands-on look at the neural networks available to build a deepfake pipeline from scratch using the PyTorch ML toolkit, starting with the models available for detecting and extracting faces from source images.
