用人脸替换技术保护视频隐私，兼顾一致性与匿名性。
Assessing the Use of Face Swapping Methods as Face Anonymizers in Videos
- 采用人脸替换技术实现视频中身份匿名化。
- 实验验证了时序一致性与身份隐藏效果良好。
- 适合需要隐私保护的视频数据应用。
随着大规模视觉数据需求的增长和严格的隐私法规，研究旨在开发在不严重降低数据质量的前提下隐藏个人身份的匿名化方法。本文探讨了人脸替换技术在视频数据隐私保护中的潜力。通过针对时序一致性、匿名强度和视觉保真度的广泛评估，发现人脸替换技术能够产生连贯的面部过渡，并有效隐藏身份。这些结果强调了人脸替换在隐私保护视频应用中的适用性，为未来专注于人脸替换的匿名化模型发展奠定了基础。
原文摘要 · Abstract (English)
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.
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