Table of contents : Cover Preface Title Page Copyright Contributors Table of Contents Part 1: Understanding Deepfakes Chapter 1: Surveying Deepfakes Introducing deepfakes Exploring the uses of deepfakes Entertainment Parody Education Advertisements Discovering how deepfakes work Generative auto-encoders Assessing the limitations of generative AI Resolution Training required for each face pair Training data Looking at existing deepfake software Faceswap DeepFaceLab First Order Model Reface Summary Chapter 2: Examining Deepfake Ethics and Dangers The unethical origin of deepfakes Being an ethical deepfaker Consent Respect Deception Putting it into practice The dangers of deepfakes Reputation Politics Avoiding consequences by claiming manipulation Preventing damage from deepfakes Starving the model of data Authenticating any genuine media Deepfake detection Public relations Public awareness Summary Chapter 3: Acquiring and Processing Data Why data is important Understanding the value of variety Pose Expression Lighting Bringing this variety together Sourcing data Filming your own data Getting data from historical sources Improving your data Linear color Data matching Upscaling Summary Chapter 4: The Deepfake Workflow Technical requirements Identifying suitable candidates for a swap Preparing the training images Extracting faces from your source data Curating training images Training a model Setting up Launching and monitoring training Manual intervention Applying a trained model to perform a swap The alignments file Cleaning the alignments file Fixing the alignments file Using the Preview tool Generating the swap Summary Part 2: Getting Hands-On with the Deepfake Process Chapter 5: Extracting Faces Technical requirements Getting image files from a video Running extract on frame images face_alignments.json face_bbox_{filename}_{face number}.png face_aligned_{filename}_{face number}.png face_mask_{filename}_{face number}.png Getting hands-on with the code Initialization Image preparation Face detection Face landmarking/aligning Summary Exercises Chapter 6: Training a Deepfake Model Technical requirements Understanding convolutional layers Getting hands-on with AI Defining our upscaler Creating the encoder Building the decoders Exploring the training code Creating our models Looping over the training Teaching the network Saving results Summary Exercises Chapter 7: Swapping the Face Back into the Video Technical requirements Preparing to convert video Getting hands-on with the convert code Initialization Loading the AI Preparing data The conversion loop Creating the video from images Summary Exercises Part 3: Where to Now? Chapter 8: Applying the Lessons of Deepfakes Technical requirements Aligning other types of images Finding an aligner Using the library Using the landmarks to align The power of masking images Types of masking Finding a usable mask for your object Examining an example Getting data under control Defining your rules Evolving your rules Dealing with errors Summary Chapter 9: The Future of Generative AI Generating text Recent developments Building sentences The future of text generation Improving image quality Various tactics The future of image quality upgrading Text-guided image generation CLIP Image generation with CLIP The future of image generation Generating sound Voice swapping Text-guided music generation The future of sound generation Deepfakes Sound generation Text-guided image generation Improving image quality Text generation The future of deepfakes The future of AI ethics Summary Index About Packt Other Books You May Enjoy