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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?

Upscaling

One major current trend in AI is in upscaling content. This is a great use for AI, which can fill in missing data from its training, especially temporally aware upscalers, which can find missing data by tracking an object across multiple frames to get more detail. Unfortunately, when used as training data for generative AI such as deepfakes, the AI upscaled data is problematic and prone to training failures. Even a very good upscaling AI has glitches and artifacts. The artifacts might be difficult for the eye to see, but the deepfake AI searches for patterns and will often get tripped up by artifacts, causing the training to fail.

Generally, the best way to deal with upscaling is to not upscale your training data but instead to upscale the output. This is even better in many ways since it can replace missing face data and improve the resolution of the output at the same time. The reason that chaining AIs in that direction doesn’t cause failures is that, unlike deepfakes...

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