无需训练即可保护人脸身份，同时保留年龄性别等真实属性。
FLUID: Training-Free Face De-identification via Latent Identity Substitution
- 在预训练扩散模型的隐空间直接替换身份特征，不修改模型权重。
- 在CelebA-HQ和FFHQ上实现更强的身份隐藏与属性保留平衡。
- 适合需要快速部署且不希望牺牲图像真实性的隐私保护场景。
现有面部去标识方法在人脸区域替换可识别线索时，会损害影响真实感的属性（如年龄、性别）。为恢复受损的真实感，我们提出FLUID（基于隐空间身份位移的无训练人脸去标识），一种仅需单输入的去标识框架，通过在预训练扩散模型的隐空间中直接替换身份特征，而不改变模型权重。我们将人脸去标识重新定义为预训练无条件扩散模型隐空间中的图像编辑任务。通过损失函数引导的优化，估计身份编辑方向，以鼓励属性保持并抑制身份信号。进一步引入线性和测地线（切向）编辑方案，有效导航隐空间流形。在CelebA-HQ和FFHQ上的实验表明，FLUID在身份抑制与属性保持之间实现了更优平衡，优于现有去标识方法，在定性和定量评估中均表现更佳。
原文摘要 · Abstract (English)
Current face de-identification methods that replace identifiable cues in the face region with other sacrifices utilities contributing to realism, such as age and gender. To retrieve the damaged realism, we present FLUID (Face de-identification in the Latent space via Utility-preserving Identity Displacement), a single-input face de-identification framework that directly replaces identity features in the latent space of a pretrained diffusion model without affecting the model's weights. We reinterpret face de-identification as an image editing task in the latent h-space of a pretrained unconditional diffusion model. Our framework estimates identity-editing directions through optimization guided by loss functions that encourage attribute preservation while suppressing identity signals. We further introduce both linear and geodesic (tangent-based) editing schemes to effectively navigate the latent manifold. Experiments on CelebA-HQ and FFHQ show that FLUID achieves a superior balance between identity suppression and attribute preservation, outperforming existing de-identification approaches in both qualitative and quantitative evaluations.
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