Book Image

Exploring Deepfakes

By : Bryan Lyon, Matt Tora
Book Image

Exploring Deepfakes

By: Bryan Lyon, Matt Tora

Overview of this book

Applying Deepfakes will allow you to tackle a wide range of scenarios creatively. Learning from experienced authors will help you to intuitively understand what is going on inside the model. You’ll learn what deepfakes are and what makes them different from other machine learning techniques, and understand the entire process from beginning to end, from finding faces to preparing them, training the model, and performing the final swap. We’ll discuss various uses for face replacement before we begin building our own pipeline. Spending some extra time thinking about how you collect your input data can make a huge difference to the quality of the final video. We look at the importance of this data and guide you with simple concepts to understand what your data needs to really be successful. No discussion of deepfakes can avoid discussing the controversial, unethical uses for which the technology initially became known. We’ll go over some potential issues, and talk about the value that deepfakes can bring to a variety of educational and artistic use cases, from video game avatars to filmmaking. By the end of the book, you’ll understand what deepfakes are, how they work at a fundamental level, and how to apply those techniques to your own needs.
Table of Contents (15 chapters)
1
Part 1: Understanding Deepfakes
6
Part 2: Getting Hands-On with the Deepfake Process
10
Part 3: Where to Now?

Running extract on frame images

To run the extract process on a video, you can run the extract program from the cloned git repository folder. To do this, simply run the following in an Anaconda prompt:

cd {Folder of the downloaded git repo}\
python C5-face_detection.py {Folder of frame images}

This will run the face extraction process on each of the images in the folder and put the extracted images into the face_images folder, which (by default) will be inside the folder of frame images. This folder will contain three types of files for each face detected, along with a file containing all alignments.

face_alignments.json

There will just be one of these files. It is a JSON-formatted file containing the landmark positions and warp matrix for every face found in the images. This file is human-readable like any JSON file and can be read or edited (though it’s probably not something that you’d do manually).

face_landmarks_{filename}_{face number}.png

This...