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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?

Aligning other types of images

Aligning faces is a critical tool for getting deepfakes to work. Without the alignment of faces, we’d be doomed with extremely long training times and huge models to correct the faces. It’s not a stretch to say that without alignment, modern deepfakes would effectively be impossible today.

Alignment saves time and compute power by removing the need for the neural network to figure out where the face is in the image and adapt for the many different locations the face may be. By aligning in advance, the AI doesn’t even need to learn what a face is in order to do its job. This allows the AI to focus on learning the task at hand, such as generating realistic facial expressions or speech, rather than trying to locate and correct misaligned faces.

In addition to improving the efficiency of the training process, aligning faces also helps to improve the quality and consistency of the final deepfake. Without proper alignment, the generated...

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