Fair and Interpretable Deepfake Detection in Videos

TL;DR

This paper introduces a fairness-aware deepfake detection framework that combines temporal feature learning, demographic-aware data augmentation, and interpretability techniques to improve bias mitigation and reliability across diverse groups.

Contribution

It presents a novel framework integrating temporal modeling, demographic-aware augmentation, and concept extraction for fairer, more interpretable deepfake detection.

Findings

Achieves the best tradeoff between fairness and accuracy on multiple datasets.

Effectively mitigates bias across demographic groups.

Enhances interpretability for non-expert users.

Abstract

Existing deepfake detection methods often exhibit bias, lack transparency, and fail to capture temporal information, leading to biased decisions and unreliable results across different demographic groups. In this paper, we propose a fairness-aware deepfake detection framework that integrates temporal feature learning and demographic-aware data augmentation to enhance fairness and interpretability. Our method leverages sequence-based clustering for temporal modeling of deepfake videos and concept extraction to improve detection reliability while also facilitating interpretable decisions for non-expert users. Additionally, we introduce a demography-aware data augmentation method that balances underrepresented groups and applies frequency-domain transformations to preserve deepfake artifacts, thereby mitigating bias and improving generalization. Extensive experiments on FaceForensics++, DFD, Celeb-DF, and DFDC datasets using state-of-the-art (SoTA) architectures (Xception, ResNet) demonstrate the efficacy of the proposed method in obtaining the best tradeoff between fairness and accuracy when compared to SoTA.