High-Level Workflow

Explore the high-level workflow behind deepfake creation, focusing on how source and target faces are processed, features extracted, and realistic fake images generated with GANs and encoder-decoder architectures. Learn key steps including face detection, feature extraction, generation, and blending to produce believable synthetic content.

We'll cover the following...

Common architectures
Encoder-decoder (ED)
Generative adversarial networks (GANs)

Fake content generation is a complex task consisting of a number of components and steps that help in generating believable content. While this space is seeing quite a lot of research and hacks that improve the overall results, the setup can largely be explained using a few common building blocks. In this lesson, we’ll discuss a common high-level flow that describes how a deepfake setup uses data to train and generate fake content. We'll also touch upon a few common architectures used in a number of works as basic building blocks.

As discussed earlier, a deepfake setup requires a source identity ( $x_s$ ) which drives the target identity ( $x_t$ ...

1.Introduction to the Course

2.An Introduction to Generative AI

3.Building Blocks of Deep Neural Networks

4.Teaching Networks to Generate Digits

5.Painting Pictures with Neural Networks Using VAEs

Project

6.Image Generation with GANs

Project

7.Style Transfer with GANs

Assessment

8.Deepfakes with GANs

9.The Rise of Methods for Text Generation

10.NLP 2.0: Using Transformers to Generate Text

11.Composing Music with Generative Models

Project

12.Play Video Games with Generative AI: GAIL

13.Emerging Applications in Generative AI

Assessment

14.Conclusion

15.Appendix

High-Level Workflow