Assessing the Use of Face Swapping Methods as Face Anonymizers in Videos

2025/05/27 by Mustafa İzzet Muştu, Muştu, Mustafa İzzet, Hazım Kemal Ekenel +1
Computer Science · Social Sciences · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Law in Society and Culture

paper · pdf · doi:10.48550/arxiv.2505.20985

openalex publication_date 2025/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The increasing demand for large-scale visual data, coupled with strict privacy regulations, has driven research into anonymization methods that hide personal identities without seriously degrading data quality. In this paper, we explore the potential of face swapping methods to preserve privacy in video data. Through extensive evaluations focusing on temporal consistency, anonymity strength, and visual fidelity, we find that face swapping techniques can produce consistent facial transitions and effectively hide identities. These results underscore the suitability of face swapping for privacy-preserving video applications and lay the groundwork for future advancements in anonymization focused face-swapping models.

Citations

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• Sample and Computation Redistribution for Efficient Face Detection
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• Analyzing and Improving the Image Quality of StyleGAN
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Related

• Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy
• BLANKET: Anonymizing Faces in Infant Video Recordings
• CanonSwap: High-Fidelity and Consistent Video Face Swapping via Canonical Space Modulation
• Password-conditioned Anonymization and Deanonymization with Face Identity Transformers
• FAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentification