In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably. However, there is an increased concern that these models and training databases might be biased and, thus, cause Deepfake detectors to fail. In this work, we investigate the bias issue caused by public Deepfake datasets by (a) providing large-scale demographic and non-demographic attribute annotations of 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets. The investigation analyses the influence of a large variety of distinctive attributes (from over 65M labels) on the detection performance, including demographic (age, gender, ethnicity) and non-demographic (hair, skin, accessories, etc.) information. The results indicate that investigated databases lack diversity and, more importantly, show that the utilised Deepfake detection backbone models are strongly biased towards many investigated attributes. The Deepfake detection backbone methods, which are trained with biased datasets, might output incorrect detection results, thereby leading to generalisability, fairness, and security issues. We hope that the findings of this study and the annotation databases will help to evaluate and mitigate bias in future Deepfake detection techniques. The annotation datasets are publicly available.

翻译：近年来，利用深度伪造技术进行的图像和视频篡改已成为社会安全领域的严重隐患。研究者已提出大量检测模型与数据集以可靠识别深度伪造数据，但日益增长的担忧表明，这些模型和训练数据库可能本身存在偏见，进而导致深度伪造检测器失效。本研究通过以下两方面深入探讨公开深度伪造数据集引发的偏见问题：（a）为五个主流深度伪造数据集提供涵盖47种不同属性的大规模人口统计与非人口统计特征注释；（b）全面分析三种前沿深度伪造检测骨干模型在这些数据集上的AI偏见。研究从超过6500万个标签中系统分析了多种独特属性（包括年龄、性别、种族等人口统计特征及发型、肤色、配饰等非人口统计特征）对检测性能的影响。结果表明，所研究数据库存在多样性不足的问题，更重要的是，当前采用的深度伪造检测骨干模型对多种被分析属性表现出显著偏见。使用存在偏见数据集训练的深度伪造检测骨干方法可能输出错误检测结果，从而引发泛化性、公平性与安全性问题。我们期望本研究成果及标注数据库能为未来深度伪造检测技术的偏见评估与消除提供助力。相关标注数据集将公开提供。