  Despite the development of effective deepfake detectors in recent years,
recent studies have demonstrated that biases in the data used to train these
detectors can lead to disparities in detection accuracy across different races
and genders. This can result in different groups being unfairly targeted or
excluded from detection, allowing undetected deepfakes to manipulate public
opinion and erode trust in a deepfake detection model. While existing studies
have focused on evaluating fairness of deepfake detectors, to the best of our
knowledge, no method has been developed to encourage fairness in deepfake
detection at the algorithm level. In this work, we make the first attempt to
improve deepfake detection fairness by proposing novel loss functions that
handle both the setting where demographic information (eg, annotations of race
and gender) is available as well as the case where this information is absent.
Fundamentally, both approaches can be used to convert many existing deepfake
detectors into ones that encourages fairness. Extensive experiments on four
deepfake datasets and five deepfake detectors demonstrate the effectiveness and
flexibility of our approach in improving deepfake detection fairness. Our code
is available at https://github.com/littlejuyan/DF_Fairness.
