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

• URL: http://arxiv.org/abs/2003.00813v1
• Date: Fri, 7 Feb 2020 22:36:48 GMT
• Title: Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation
• Authors: Bingquan Zhu, Hao Fang, Yanan Sui, Luming Li
• Abstract summary: Face-swapping as a de-identification approach is reliable, and it keeps the keypoints almost invariant, significantly better than traditional methods. This study proposes a pipeline for video de-identification and keypoint preservation, clearing up some ethical restrictions for medical data sharing.
• Score: 12.10092482860325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data sharing for medical research has been difficult as open-sourcing clinical data may violate patient privacy. Traditional methods for face de-identification wipe out facial information entirely, making it impossible to analyze facial behavior. Recent advancements on whole-body keypoints detection also rely on facial input to estimate body keypoints. Both facial and body keypoints are critical in some medical diagnoses, and keypoints invariability after de-identification is of great importance. Here, we propose a solution using deepfake technology, the face swapping technique. While this swapping method has been criticized for invading privacy and portraiture right, it could conversely protect privacy in medical video: patients' faces could be swapped to a proper target face and become unrecognizable. However, it remained an open question that to what extent the swapping de-identification method could affect the automatic detection of body keypoints. In this study, we apply deepfake technology to Parkinson's disease examination videos to de-identify subjects, and quantitatively show that: face-swapping as a de-identification approach is reliable, and it keeps the keypoints almost invariant, significantly better than traditional methods. This study proposes a pipeline for video de-identification and keypoint preservation, clearing up some ethical restrictions for medical data sharing. This work could make open-source high quality medical video datasets more feasible and promote future medical research that benefits our society.

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