Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising
This addresses fairness issues in deepfake detection for public trust, but it is incremental as it builds on existing detection methods with a novel post-processing technique.
The paper tackled the problem of bias in deepfake detection across demographic attributes like ethnicity and gender, proposing Fair-FLIP, a post-processing method that improved fairness metrics by up to 30% while maintaining baseline accuracy with only a 0.25% reduction.
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