Recent studies have demonstrated that deep learning models can discriminate
based on protected classes like race and gender. In this work, we evaluate bias
present in deepfake datasets and detection models across protected subgroups.
Using facial datasets balanced by race and gender, we examine three popular
deepfake detectors and find large disparities in predictive performances across
races, with up to 10.7% difference in error rate between subgroups. A closer
look reveals that the widely used FaceForensics++ dataset is overwhelmingly
composed of Caucasian subjects, with the majority being female Caucasians. Our
investigation of the racial distribution of deepfakes reveals that the methods
used to create deepfakes as positive training signals tend to produce
"irregular" faces - when a person's face is swapped onto another person of a
different race or gender. This causes detectors to learn spurious correlations
between the foreground faces and fakeness. Moreover, when detectors are trained
with the Blended Image (BI) dataset from Face X-Rays, we find that those
detectors develop systematic discrimination towards certain racial subgroups,
primarily female Asians.

研究发现最近深度学习模型容易出现以种族和性别等保护类别为基础的歧视。通过使用平衡的脸部数据集，探究三种常见的深度伪造检测器在保护子群体上的性能差异，发现预测性能在种族之间存在很大的差异，该研究还发现一些方法可能会导致伪造影响保护子群体，并造成检测器出现系统性别种族歧视。