  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.
