  Although effective deepfake detection models have been developed in recent
years, recent studies have revealed that these models can result in unfair
performance disparities among demographic groups, such as race and gender. This
can lead to particular groups facing unfair targeting or exclusion from
detection, potentially allowing misclassified deepfakes to manipulate public
opinion and undermine trust in the model. The existing method for addressing
this problem is providing a fair loss function. It shows good fairness
performance for intra-domain evaluation but does not maintain fairness for
cross-domain testing. This highlights the significance of fairness
generalization in the fight against deepfakes. In this work, we propose the
first method to address the fairness generalization problem in deepfake
detection by simultaneously considering features, loss, and optimization
aspects. Our method employs disentanglement learning to extract demographic and
domain-agnostic forgery features, fusing them to encourage fair learning across
a flattened loss landscape. Extensive experiments on prominent deepfake
datasets demonstrate our method's effectiveness, surpassing state-of-the-art
approaches in preserving fairness during cross-domain deepfake detection. The
code is available at https://github.com/Purdue-M2/Fairness-Generalization
