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Exploring Deepfakes

You're reading from   Exploring Deepfakes Deploy powerful AI techniques for face replacement and more with this comprehensive guide

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Product type Paperback
Published in Mar 2023
Publisher Packt
ISBN-13 9781801810692
Length 192 pages
Edition 1st Edition
Languages
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Authors (2):
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Matt Tora Matt Tora
Author Profile Icon Matt Tora
Matt Tora
Bryan Lyon Bryan Lyon
Author Profile Icon Bryan Lyon
Bryan Lyon
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Table of Contents (15) Chapters Close

Preface 1. Part 1: Understanding Deepfakes
2. Chapter 1: Surveying Deepfakes FREE CHAPTER 3. Chapter 2: Examining Deepfake Ethics and Dangers 4. Chapter 3: Acquiring and Processing Data 5. Chapter 4: The Deepfake Workflow 6. Part 2: Getting Hands-On with the Deepfake Process
7. Chapter 5: Extracting Faces 8. Chapter 6: Training a Deepfake Model 9. Chapter 7: Swapping the Face Back into the Video 10. Part 3: Where to Now?
11. Chapter 8: Applying the Lessons of Deepfakes 12. Chapter 9: The Future of Generative AI 13. Index 14. Other Books You May Enjoy

Exploring the training code

Now that we have defined our models, we can go ahead with the process of training a neural network on our data. This is the part where we actually have AI learn the different faces so that it can later swap between them.

  1. First, we import our libraries:
    from glob import glob
    import os
    import random
    from argparse import ArgumentParser
    import cv2
    import numpy as np
    from tqdm import tqdm
    import torch
    from lib.models import OriginalEncoder, OriginalDecoder

Like all Python programs, we import our libraries. We also import our encoder and decoders from our model file. This loads the AI model code from earlier in this chapter and lets us use those to define our models in this code. Python really makes it easy to import code we’ve already written, as every Python file can be called directly or imported into another file.

Note that Python uses a strange syntax for folder paths. Python treats this syntax exactly the same as a module, so you...

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