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?

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

activation function 105

Adaptive Weighting Regression (AWR) for 3D Hand Pose Estimation 142

Adobe RGB 31

AI ethics

future 164

AI hands-on 103

decoder, building 107-109

encoder, creating 104-107

upscaler, defining 103

align 37

aligner 145

searching 141, 142

alignments file 57

fixing 61-64

ambient 29

artificial intelligence (AI) 88

attention layer 151

B

beam 152

bicubic filtering 33

BiSeNet-FP 40

blue, green, red (BGR) 116

bootstrapping 146

bottleneck 106

C

camera intrinsics 144

central processing unit (CPU) 112

chatbot 151

Chinese Room 152

CLIP 147, 157

CLIPseg 147

code

face detection 90, 91

face landmarking/aligning 92-97

image preparation 89, 90

initialization 85-89

code conversion...