# Cost Sensitive Optimization of Deepfake Detector

arXiv: https://arxiv.org/abs/2012.04199 · HTML: https://mlsanity.com/paper/2012.04199/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

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

- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis](https://mlsanity.com/paper/2405.00355/) (2024)
- [DFGC 2022: The Second DeepFake Game Competition](https://mlsanity.com/paper/2206.15138/) (2022)
- [How Close are Other Computer Vision Tasks to Deepfake Detection?](https://mlsanity.com/paper/2310.00922/) (2023)
- [Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection](https://mlsanity.com/paper/2502.07269/) (2025)

## Abstract (arXiv)

Since the invention of cinema, the manipulated videos have existed. But generating manipulated videos that can fool the viewer has been a time-consuming endeavor. With the dramatic improvements in the deep generative modeling, generating believable looking fake videos has become a reality. In the present work, we concentrate on the so-called deepfake videos, where the source face is swapped with the targets. We argue that deepfake detection task should be viewed as a screening task, where the user, such as the video streaming platform, will screen a large number of videos daily. It is clear then that only a small fraction of the uploaded videos are deepfakes, so the detection performance needs to be measured in a cost-sensitive way. Preferably, the model parameters also need to be estimated in the same way. This is precisely what we propose here.