Introduction: Deepfakes with GANs

In this chapter, we’ll cover different concepts, architectures, and components associated with deepfakes. We'll focus on the following topics:

• Overview of the deepfakes technological landscape.

• The different forms of deepfaking: replacement, re-enactment, and editing.

• Key features leveraged by different architectures.

• A high-level deepfakes workflow.

• Swapping faces using autoencoders.

• Re-enacting Obama’s facial movements using pix2pix.

• Challenges and ethical issues.

• A brief discussion of off-the-shelf implementations.

We’ll cover the internal workings of different GAN architectures and key contributions that have enabled deepfakes. We’ll also build and train these architectures from scratch to better understand them. Deepfakes are not limited to videos or photographs but are also used to generate fake text (news articles, books) and even audio (voice clips, phone calls). In this chapter, we’ll focus on videos/images only, and the term deepfakes refers to related use cases unless stated otherwise.

Let’s begin with an overview of deepfakes.