# Amplifying The Uncanny

arXiv: https://arxiv.org/abs/2002.06890 · HTML: https://mlsanity.com/paper/2002.06890/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Active Divergence with Generative Deep Learning -- A Survey and Taxonomy](https://mlsanity.com/paper/2107.05599/) (2021)
- [Challenges in creative generative models for music: a divergence maximization perspective](https://mlsanity.com/paper/2211.08856/) (2022)
- [Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities](https://mlsanity.com/paper/2107.01858/) (2021)
- [Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks](https://mlsanity.com/paper/2111.02175/) (2021)
- [Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI](https://mlsanity.com/paper/2609.03800/) (2026)

## Abstract (arXiv)

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.