# How Close are Other Computer Vision Tasks to Deepfake Detection?

Source: [How Close are Other Computer Vision Tasks to Deepfake Detection?](<https://www.sota2.com/research/paper/68fa70b61e209131cea4cc07>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:47:58.595Z

Authors: Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

Published: 2023-10-02T06:32:35.000Z

## Abstract

In this paper, we challenge the conventional belief that supervised ImageNet-trained models have strong generalizability and are suitable for use as feature extractors in deepfake detection. We present a new measurement, "model separability," for visually and quantitatively assessing a model's raw capacity to separate data in an unsupervised manner. We also present a systematic benchmark for determining the correlation between deepfake detection and other computer vision tasks using pre-trained models. Our analysis shows that pre-trained face recognition models are more closely related to deepfake detection than other models. Additionally, models trained using self-supervised methods are more effective in separation than those trained using supervised methods. After fine-tuning all models on a small deepfake dataset, we found that self-supervised models deliver the best results, but there is a risk of overfitting. Our results provide valuable insights that should help researchers and practitioners develop more effective deepfake detection models.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.00922>)

[PDF](<https://arxiv.org/pdf/2310.00922.pdf>)