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?

Getting hands-on with the convert code

Like the rest of the chapters in this section, we’ll be going through the code line by line to talk about how it works and what it’s doing.

Initialization

Here we will initialize and prepare the code to run the convert process:

  1. Like all Python code, we’ll start with the imports:
    import os
    from argparse import ArgumentParser
    import json_tricks
    import torch
    import cv2
    import numpy as np
    from tqdm import tqdm
    import face_alignment
    from face_alignment.detection.sfd import FaceDetector
    from face_alignment import FaceAlignment, LandmarksType
    from lib.bisenet import BiSeNet
    from lib.models import OriginalEncoder, OriginalDecoder

These libraries are all ones we’ve already seen in previous chapters. This is because the conversion process is not really doing anything too different from what we’ve done before. We’ll see that as we go through the code to covert the face back into the original images...