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Data sharing for medical research has been difficult as open-sourcingclinical data may violate patient privacy. Traditional methods for facede-identification wipe out facial information entirely, making it impossible toanalyze facial behavior. Recent advancements on whole-body keypoints detectionalso rely on facial input to estimate body keypoints. Both facial and bodykeypoints are critical in some medical diagnoses, and keypoints invariabilityafter de-identification is of great importance. Here, we propose a solutionusing deepfake technology, the face swapping technique. While this swappingmethod has been criticized for invading privacy and portraiture right, it couldconversely protect privacy in medical video: patients' faces could be swappedto a proper target face and become unrecognizable. However, it remained an openquestion that to what extent the swapping de-identification method could affectthe automatic detection of body keypoints. In this study, we apply deepfaketechnology to Parkinson's disease examination videos to de-identify subjects,and quantitatively show that: face-swapping as a de-identification approach isreliable, and it keeps the keypoints almost invariant, significantly betterthan traditional methods. This study proposes a pipeline for videode-identification and keypoint preservation, clearing up some ethicalrestrictions for medical data sharing. This work could make open-source highquality medical video datasets more feasible and promote future medicalresearch that benefits our society.

Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation