The Zizi Show

Process

Machine learning – teaching computers to learn from data – and specifically deepfake technology, has been used to construct all the videos you see. To produce a deepfake, you start by training a neural network¹ on a dataset of images.

This dataset contains the original images (video frames) of the real-life person, as well as a graphic tracking the position of their skeleton, facial features, and silhouette.

Creating deepfakes begins with training a neural network to try to recreate the original image of this person based on just their skeleton tracking (illustrated below). The neural network aims to get as close as possible to the original and does this by being given an accuracy score.² Once it has learnt to do this it can then start producing deepfakes.

Below, you can see the iterative training process of a neural network as it learns how to create images of the drag queen an burlesque artist Lilly SnatchDragon:

Using machine learning, this process iterates and improves until it can create new, fake faces which are indistinguishable from the real. For Zizi, the method I use is called Video-to-Video Synthesis. ³

Once the neural network has been training for, let's say, three days, it is ready to be fed new movements. Anyone can now control the deepfake body by running skeleton tracking on a new video and then feeding these into the neural network.

These visuals below show how new deepfake images of Lilly SnatchDragon can be generated from the trained neural network (here with her movement controlled by Me The Drag Queen).

This process was repeated to create deepfakes of all 21 of our wonderful, diverse drag cast.

The ‘Zizi’ character was created by simultaneously training on images of all of the performers. Not knowing how to differentiate between the bodies, the result is an amalgamation, a ‘queering’ of the data.

Facial recognition algorithms (and deepfake technology) currently have difficulty recognising trans, queer and other marginalised identities, because they are often made by heteronormative white people.^4,5

The project asks whether making deepfakes using queer identities becomes a means of assimilation or inclusivity… or more a techno-activist method of dirtying and obfuscating the systems used to collect data on us.

The Zizi project aims to critically examine these techniques using a dataset of drag performers, in the process exposing the workings of the black box which is artificial intelligence.