Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection

TL;DR

This paper introduces misleading-learning, a novel approach that enhances deepfake detection fairness by populating the model's latent space with diverse demographic information, reducing bias and improving generalization.

Contribution

The paper proposes misleading-learning to mitigate demographic bias in deepfake detectors by creating redundant environments in the latent space for better fairness.

Findings

Achieves superior fairness compared to state-of-the-art methods

Improves cross-domain generalization in deepfake detection

Maintains high detection performance while reducing demographic bias

Abstract

Detecting falsified faces generated by Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals. However, current detectors often suffer from a dual-overfitting: they become overly specialized in both specific forgery fingerprints and particular demographic attributes. Critically, most existing methods overlook the latter issue, which results in poor fairness: faces from certain demographic groups, such as different genders or ethnicities, are consequently more difficult to reliably detect. To address this challenge, we propose a novel strategy called misleading-learning, which populates the latent space with a multitude of redundant environments. By exposing the detector to a sufficiently rich and balanced variety of high-level information for demographic fairness, our approach mitigates demographic bias while maintaining a high detection performance level. We conduct extensive evaluations on fairness, intra-domain detection, cross-domain generalization, and robustness. Experimental results demonstrate that our framework achieves superior fairness and generalization compared to state-of-the-art approaches.