# Limits of Deepfake Detection: A Robust Estimation Viewpoint

arXiv: https://arxiv.org/abs/1905.03493 · HTML: https://mlsanity.com/paper/1905.03493/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2019.

## Built on by (most cited first, 3 of 3)

- [Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection](https://mlsanity.com/paper/2507.14807/) (2025)
- [Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising](https://mlsanity.com/paper/2507.08912/) (2025)
- [Detection of fake faces in videos](https://mlsanity.com/paper/2201.12051/) (2022)

## Abstract (arXiv)

Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the error probability for various GAN implementations in terms of their performance. The bounds are further simplified using a Euclidean approximation for the low error regime. Lastly, relationships between error probability and epidemic thresholds for spreading processes in networks are established.