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?

Part 2: Getting Hands-On with the Deepfake Process

This part of the book is all about getting hands-on with the code. We will look deep into exactly what it takes to make a deepfake from beginning to end, leaving no stone unturned or line of code unexplained. If you’re here for the code, this is the section for you.

In the first chapter of this section, we’ll examine extraction. This is the process of getting all the faces out of a video so that we can use them in other stages of the process. We’ll look at the process of turning a video into frame images, then we’ll go through all the code necessary to turn the frames into clean, aligned faces with matching mask images ready for training. After that, we’ll look into training, examine the neural network from the bottom up, and then show the entire learning process of the model. Finally, we’ll get into conversion, where we’ll examine the process of going through every image to swap...