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Exploring Deepfakes

Exploring Deepfakes

By : Bryan Lyon, Matt Tora
4.5 (6)
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Exploring Deepfakes

Exploring Deepfakes

4.5 (6)
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)
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1
Part 1: Understanding Deepfakes
6
Part 2: Getting Hands-On with the Deepfake Process
10
Part 3: Where to Now?

Preparing the training images

In this section, we will be collecting, extracting, and curating the images to train our model. Far and away the best sources for collecting face data are video files. Videos are just a series of still images, but as you can obtain 25 still images for every second of video in a standard 25 FPS file, they are a valuable and plentiful resource. Video is also likely to contain a lot more natural and varied poses than photographs, which tend to be posed and contain limited expressions.

Video sources should be of a high quality. The absolute best source of data is HD content encoded at a high bitrate. You should be wary of video content acquired from online streaming platforms, as these tend to be of a low bitrate, even if the resolution is high. For similar reasons, JPEG images can also be problematic. The neural network will learn to recreate what it sees, and this will include learning compression artifacts from low-bitrate/highly compressed sources....

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