Replacement Using Autoencoders

The aim of this exercise is to develop a face swapping setup. In this setup, we'll focus on transforming Nicolas Cage (a Hollywood actor) into Donald J. Trump (former US president).

Disclaimer: This demonstration uses deepfake technology to show how images and videos can be manipulated using AI. It is intended purely for educational purposes to explore the capabilities of the technology, not to deceive or mislead. Ethical considerations are crucial when using this technology, and unauthorized use without consent is not supported.

Autoencoder architecture

We have prepared our datasets for both Donald Trump and Nicolas Cage using the tools presented in the previous lesson. Let’s now work toward a model architecture that learns the task of face swapping.

We presented a few common architectures in earlier sections of the course. The encoder-decoder setup is one such setup widely used for deepfake tasks. For our current task of face swapping, we will develop an autoencoder setup to learn and swap faces. As has been the norm, we’ll use TensorFlow and Keras to prepare the required models.

Before we get onto the actual architecture code, let’s briefly recap how this setup works. A typical autoencoder has two components, an encoder and a decoder. The encoder takes an image as input and compresses it down to a lower dimensional space. This compressed representation is called an embedding or bottleneck features. The decoder works in the reverse manner. It takes the embedding vector as input and tries to reconstruct the image as output. In short, an autoencoder can be described as: