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

arXiv: https://arxiv.org/abs/2003.00813 · HTML: https://mlsanity.com/paper/2003.00813/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [When do GANs replicate? On the choice of dataset size](https://mlsanity.com/paper/2202.11765/) (2022)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [FICGAN: Facial Identity Controllable GAN for De-identification](https://mlsanity.com/paper/2110.00740/) (2021)
- [Deep Learning Approaches for Seizure Video Analysis: A Review](https://mlsanity.com/paper/2312.10930/) (2023)
- [DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization](https://mlsanity.com/paper/2103.02484/) (2021)
- [Introducing Explicit Gaze Constraints to Face Swapping](https://mlsanity.com/paper/2305.16138/) (2023)
- [Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy](https://mlsanity.com/paper/2204.03559/) (2022)
- [Towards mitigating uncann(eye)ness in face swaps via gaze-centric loss terms](https://mlsanity.com/paper/2402.03188/) (2024)
- [SecurePose: Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings](https://mlsanity.com/paper/2402.14143/) (2024)
- [Targeted Therapy in Data Removal: Object Unlearning Based on Scene Graphs](https://mlsanity.com/paper/2412.00067/) (2024)
- [SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosis](https://mlsanity.com/paper/2512.08953/) (2025)
- [ReGenHuman: Re-Generating Human Appearances for Realistic Full-Body Video Anonymization](https://mlsanity.com/paper/2606.14972/) (2026)

## Abstract (arXiv)

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.