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?

Understanding convolutional layers

In this chapter, we’ll finally get into the meat of the neural networks behind deepfakes. A big part of how networks such as these work is a technique called convolutional layers. These layers are extremely important in effectively working with image data and form an important cornerstone of most neural networks.

A convolution is an operation that changes the shape of an object. In the case of neural networks, we use convolutional layers, which iterate a convolution over a matrix and create a new (generally smaller) output matrix. Convolutions are a way to reduce an image in size while simultaneously searching for patterns. The more convolutional layers you stack, the more complicated the patterns that can be encoded from the original image.

Figure 6.1 – An example of a convolution downscaling a full image

Figure 6.1 – An example of a convolution downscaling a full image

There are several details that define a convolutional layer. The first is dimensionality. In our...