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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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Toc

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

Exercises

  1. We used pre-existing libraries for face detection, landmarking, and aligning landmarks. There are other libraries that offer similar functionality. Not all libraries work the same way, and implementing the differences is an extremely useful exercise. Try replacing the face_alignment library with another library for detecting faces, such as https://github.com/timesler/facenet-pytorch or https://github.com/serengil/deepface. Open source has lots of useful libraries but learning the differences and when to use one over another can be difficult, and converting between them can be a useful practice.
  2. We used 2D landmarks for alignment in this chapter, but there may be a need for 3D landmarks instead. Try replacing the following:
    face_aligner = FaceAlignment(LandmarksType._2D,
      device=device, verbose=False)

with:

face_aligner = FaceAlignment(LandmarksType._3D,
  device=device, verbose=False)

and adjust the rest of the process accordingly...

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