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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?

Preventing damage from deepfakes

There is no foolproof way to make yourself immune to deepfakes or their effects, but there are activities and actions you can take in order to reduce the dangers. In this section, we’ll examine methods that you can use to prevent as much damage as possible.

Starving the model of data

While deepfakes have been done with a minimum of data, the result is not seamless or high quality. This means that if you can simply prevent the deepfaker from getting enough data, you can prevent a deepfake from being of sufficient quality to fool anyone.

If you’re trying to protect a famous person, this tactic is much harder, since every movie, TV show, interview, or even photoshoot that is available is a source of training data for a model. In fact, it’s almost inevitable that enough data is available to target any famous person as the public nature of their job means that a lot of training data will be publicly available.

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