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?

Technical requirements

As with all machine learning techniques, deepfakes can be created on any PC with a minimum of 4 GB of RAM. However, a machine with 8 GB of RAM or higher and a GPU (a graphics card) is strongly recommended. Training a model on a CPU is likely to take months to complete, which does not make it a realistic endeavor. Graphics cards are built specifically to perform matrix calculations, which makes them ideal for machine learning tasks.

Faceswap will run on Linux, Windows, and Intel-based macOS systems. At a minimum, Faceswap should be run on a system with 4 GB of VRAM (GPU memory). Ideally, an NVIDIA GPU should be used, as AMD GPUs are not as fully featured as their Nvidia counterparts and run considerably slower. Some features that are available for NVIDIA users are not available for AMD users, due to NVIDIA’s proprietary CUDA library being accepted as an industry standard for machine learning. GPUs with more VRAM will be able to run more of the larger...