We have learned about two different image-to-image tasks so far: semantic segmentation with UNet and image reconstruction with autoencoders. Deep fakery is an image-to-image task that has a very similar underlying theory.
Imagine a scenario where you want to create an application that takes a given image of a face and changes the facial expression in a way that you want. Deep fakes come in handy in this scenario. While we will not discuss the very latest in deep fakes in this book, techniques such as few-shot adversarial learning are developed to generate realistic images with the facial expression of interest. Knowledge of how deep fakes work and GANs (which you will learn about in the next chapters) will help you identify videos that are fake videos.
In the task of deep fakery, we would have a few hundred pictures of person A and a few hundred pictures of person B. The objective is to reconstruct person B's face with the facial expression of person A and vice...
