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 factors causing biased detection in public Deepfake datasets by (a) creating large-scale demographic and non-demographic attribute annotations with 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing attributes resulting in AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets. The analysis shows how various attributes influence a large variety of distinctive attributes (from over 65M labels) on the detection performance which includes demographic (age, gender, ethnicity) and non-demographic (hair, skin, accessories, etc.) attributes. The results examined datasets show limited diversity and, more importantly, show that the utilised Deepfake detection backbone models are strongly affected by investigated attributes making them not fair across attributes. The Deepfake detection backbone methods trained on such imbalanced/biased datasets result in incorrect detection results leading to generalisability, fairness, and security issues. Our findings and annotated datasets will guide future research to evaluate and mitigate bias in Deepfake detection techniques. The annotated datasets and the corresponding code are publicly available.

翻译：近年来，利用深度伪造技术进行图像和视频篡改已成为社会与安全领域的严重威胁。为可靠检测深度伪造数据，业界已提出多种检测模型与数据集。然而，人们愈发担忧这些模型和训练数据库可能存在偏差，从而导致深度伪造检测器失效。本研究通过以下两方面探究导致公共深度伪造数据集出现偏差检测的因素：（a）为五个主流深度伪造数据集创建包含47种不同属性的大规模人口统计与非人口统计属性标注；（b）系统分析导致三种最先进深度伪造检测骨干模型产生人工智能偏差的属性特征。该分析揭示了各类属性（涵盖年龄、性别、种族等人口统计属性及发型、肤色、配饰等非人口统计属性）对检测性能的影响机制（基于超过6500万个标签）。研究结果表明，所评估的数据集多样性有限，更重要的是，所采用的深度伪造检测骨干模型受所考察属性的显著影响，导致其在各属性维度上缺乏公平性。基于此类失衡/有偏数据集训练的深度伪造检测骨干方法会产生错误检测结果，进而引发泛化性、公平性和安全性问题。本研究结果及标注数据集将为未来评估与缓解深度伪造检测技术中的偏差提供指导。标注数据集及相应代码已公开提供。