To get the most out of this book

This book is designed to build knowledge as you read through the chapters. If you’re starting with no background knowledge of deepfakes, then we suggest you start at the beginning. If you want to skip straight to the code, then you’ll want to look at Part 2 (though we hope you’ll give Part 1 a peruse once you’re ready). If you only care about what you can do with the techniques moving forward, then check out Part 3 (but I promise that the earlier parts have some juicy nuggets of information).

We use Python for all code examples in this book. If you know Python, you should be able to understand all the code samples with the help of the text. If you don’t know Python, then don’t worry! There is a lot of non-code explanation, and even the code includes hands-on explanations of what is going on in it.

All the libraries used in this book are explained when they’re used, but this book should not be considered a guide or in-depth explanation of any of the libraries. Many of these libraries have books of their own dedicated to them, and their use in this book is solely functional.

Software covered in the book		Operating system requirements
Python	Faceswap	Windows, macOS, or Linux
PyTorch	OpenCV
Pillow (PIL Fork)

We use Anaconda (https://www.anaconda.com/) for package management and sandboxing throughout this book. If you want to follow along, we highly recommend you install it from the site listed here. If you would rather use Python virtual environments, you may, but if you do, the instructions in this book will not always work without modification, especially installing the necessary packages. If you choose to use that route, you will have to find the correct version of libraries to install yourself.

If you are using the digital version of this book, we advise you access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Included in each hands-on chapter is a list of exercises. Please don’t take these as directions on what you must do, but consider them as helpers to more completely understand what it is that the code is doing and how you can use the techniques for yourself. They do not have “answers” as they are not really questions; they’re just prompts for you to find new and exciting ways to apply your knowledge.

If you do complete any of the exercises (or come up with something impressive of your own), we’d appreciate it if you would “fork” the book’s repo into your own GitHub account and show the world your accomplishment! We’d love to see what you can do with deepfakes.