  Deep neural networks have become remarkably good at producing realistic
deepfakes, images of people that (to the untrained eye) are indistinguishable
from real images. Deepfakes are produced by algorithms that learn to
distinguish between real and fake images and are optimised to generate samples
that the system deems realistic. This paper, and the resulting series of
artworks Being Foiled explore the aesthetic outcome of inverting this process,
instead optimising the system to generate images that it predicts as being
fake. This maximises the unlikelihood of the data and in turn, amplifies the
uncanny nature of these machine hallucinations.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing