# Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising

Source: [Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising](<https://www.sota2.com/research/paper/68fa79f51e209131cea8042c>)

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

Data updated: 2025-10-26T05:08:30.960Z

Authors: Tomasz Szandala, Fatima Ezzeddine, Natalia Rusin, Silvia Giordano, Omran Ayoub

Published: 2025-07-11T15:17:02.000Z

## Abstract

Artificial Intelligence-generated content has become increasingly popular, yet its malicious use, particularly the deepfakes, poses a serious threat to public trust and discourse. While deepfake detection methods achieve high predictive performance, they often exhibit biases across demographic attributes such as ethnicity and gender. In this work, we tackle the challenge of fair deepfake detection, aiming to mitigate these biases while maintaining robust detection capabilities. To this end, we propose a novel post-processing approach, referred to as Fairness-Oriented Final Layer Input Prioritising (Fair-FLIP), that reweights a trained model's final-layer inputs to reduce subgroup disparities, prioritising those with low variability while demoting highly variable ones. Experimental results comparing Fair-FLIP to both the baseline (without fairness-oriented de-biasing) and state-of-the-art approaches show that Fair-FLIP can enhance fairness metrics by up to 30% while maintaining baseline accuracy, with only a negligible reduction of 0.25%.   Code is available on Github: https://github.com/szandala/fair-deepfake-detection-toolbox

## 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.

- [Deepfake Detection on FF++ LOO](<https://www.sota2.com/research/sota/deepfake-detection-on-ff-plus-plus-loo.md>): Deepfake Detection; FF++ LOO; Acc @ 0.5: 46.85.

- [Deepfake Detection on OpenForensics](<https://www.sota2.com/research/sota/deepfake-detection-on-openforensics.md>): Deepfake Detection; OpenForensics; Accuracy@0.5: 94.86.

- [Deepfake Detection on FaceForensics++ (LOO)](<https://www.sota2.com/research/sota/deepfake-detection-on-faceforensics-plus-plus-loo.md>): Deepfake Detection; FaceForensics++ (LOO); Accuracy@0.5: 41.18.

- [Deepfake detection on OpenForensics (test)](<https://www.sota2.com/research/sota/deepfake-detection-on-openforensics-test.md>): Deepfake Detection; OpenForensics (test); Accuracy: 92.95.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.08912>)

[PDF](<https://arxiv.org/pdf/2507.08912.pdf>)