Data-Driven Fairness Generalization for Deepfake Detection

Uzoamaka Ezeakunne, Chrisantus Eze, Xiuwen Liu

2025

Abstract

Despite the progress made in deepfake detection research, recent studies have shown that biases in the training data for these detectors can result in varying levels of performance across different demographic groups, such as race and gender. These disparities can lead to certain groups being unfairly targeted or excluded. Traditional methods often rely on fair loss functions to address these issues, but they under-perform when applied to unseen datasets, hence, fairness generalization remains a challenge. In this work, we propose a data-driven framework for tackling the fairness generalization problem in deepfake detection by leveraging synthetic datasets and model optimization. Our approach focuses on generating and utilizing synthetic data to enhance fairness across diverse demographic groups. By creating a diverse set of synthetic samples that represent various demographic groups, we ensure that our model is trained on a balanced and representative dataset. This approach allows us to generalize fairness more effectively across different domains. We employ a comprehensive strategy that leverages synthetic data, a loss sharpness-aware optimization pipeline, and a multi-task learning framework to create a more equitable training environment, which helps maintain fairness across both intra-dataset and cross-dataset evaluations. Extensive experiments on benchmark deepfake detection datasets demonstrate the efficacy of our approach, surpassing state-of-the-art approaches in preserving fairness during cross-dataset evaluation. Our results highlight the potential of synthetic datasets in achieving fairness generalization, providing a robust solution for the challenges faced in deepfake detection.

Paper Citation

in Harvard Style

Ezeakunne U., Eze C. and Liu X. (2025). Data-Driven Fairness Generalization for Deepfake Detection. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 582-591. DOI: 10.5220/0013161000003890

in Bibtex Style

@conference{icaart25,
author={Uzoamaka Ezeakunne and Chrisantus Eze and Xiuwen Liu},
title={Data-Driven Fairness Generalization for Deepfake Detection},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={582-591},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013161000003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Data-Driven Fairness Generalization for Deepfake Detection
SN - 978-989-758-737-5
AU - Ezeakunne U.
AU - Eze C.
AU - Liu X.
PY - 2025
SP - 582
EP - 591
DO - 10.5220/0013161000003890
PB - SciTePress