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?

Getting data under control

There’s a common saying in the AI community that an ML scientist’s job is only 10% ML and 90% data management. This, like many such sayings, is not far from the truth. While every ML task is focused on the actual training of the model, first, you must get your data into a manageable form before you can start the training. Hours of training can be completely wasted if your data isn’t properly prepared.

Before you can start training a model, you have to decide what data it is that you’re going to train it with. That data must be gathered, cleaned, converted into the right format, and generally made ready to train. Often, this involves a lot of manual processes and verification.

Defining your rules

The most important thing in the manual process is to make sure that all your data meets your requirements and meets a consistent level of quality. To do this, you need to define exactly what “good” data means. Whether...