用扩散模型实现无需标记的高效人脸匿名化
Face Anonymization Made Simple
- 仅用重建损失训练扩散模型，无需关键点或掩码
- 在两个基准上均达到顶尖水平，同时保持面部特征与图像质量
- 可拓展用于人脸替换，适合隐私保护与内容生成场景
当前的人脸匿名化技术多依赖人脸识别模型计算身份损失，易产生误差。许多方法还需额外输入如面部关键点或掩码来引导生成。本文提出一种新方法，仅使用重建损失训练扩散模型，无需任何辅助数据即可生成具有精细细节的图像。我们在两个公开基准上通过定量与定性评估验证了效果。结果表明，该模型在身份匿名化、面部属性保留和图像质量三个关键指标上均达到当前最优表现。此外，通过引入额外人脸图像，模型还能实现人脸替换任务，展现出良好泛化能力与应用潜力。代码与模型已开源：https://github.com/hanweikung/face_anon_simple。
原文摘要 · Abstract (English)
Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast, our approach uses diffusion models with only a reconstruction loss, eliminating the need for facial landmarks or masks while still producing images with intricate, fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization, facial attribute preservation, and image quality. Beyond its primary function of anonymization, our model can also perform face swapping tasks by incorporating an additional facial image as input, demonstrating its versatility and potential for diverse applications. Our code and models are available at https://github.com/hanweikung/face_anon_simple .
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