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?

Summary

Generative AI has a huge history and a tremendous future. We’re standing before a vast plane where anything is possible, and we just have to go toward it. That said, not everything is visible today, and we must temper our expectations. The main challenges are the limitations of our computers, time, and research. If we dedicate our time and efforts to solving some of AI’s limitations, we’ll inevitably come up with brand-new leaps that will help us move forward. Even without huge revolutionary improvements though, there are a lot of smaller evolutionary improvements that we can make to improve the capabilities of these models.

The biggest driver of innovation is need. Having more and more people using generative AI and putting it toward novel uses will create the economic and social pushes that generative AI needs to continue being improved on into the future.

This book has all been about getting us to this point where we, the authors, can invite you...