NullFace：无需训练的局部人脸匿名化技术

摘要

在当今数字化时代，随着摄像头数量的不断增长，隐私问题日益凸显。尽管现有的匿名化方法能够隐藏身份信息，但它们往往难以保持图像的实用性。在本研究中，我们提出了一种无需训练的匿名化方法，用于面部处理，同时保留关键的非身份相关属性。我们的方法利用预训练的文本到图像扩散模型，无需优化或训练。首先，通过反转输入图像恢复其初始噪声。随后，通过一个基于身份条件的扩散过程对噪声进行去噪，其中修改后的身份嵌入确保匿名化后的面部与原始身份不同。我们的方法还支持局部匿名化，让用户能够控制哪些面部区域被匿名化或保持原样。与最先进方法的全面对比评估显示，我们的方法在匿名化、属性保留和图像质量方面表现出色。其灵活性、鲁棒性和实用性使其非常适合实际应用。代码和数据可在https://github.com/hanweikung/nullface 获取。

English

Privacy concerns around ever increasing number of cameras are increasing in today's digital age. Although existing anonymization methods are able to obscure identity information, they often struggle to preserve the utility of the images. In this work, we introduce a training-free method for face anonymization that preserves key non-identity-related attributes. Our approach utilizes a pre-trained text-to-image diffusion model without requiring optimization or training. It begins by inverting the input image to recover its initial noise. The noise is then denoised through an identity-conditioned diffusion process, where modified identity embeddings ensure the anonymized face is distinct from the original identity. Our approach also supports localized anonymization, giving users control over which facial regions are anonymized or kept intact. Comprehensive evaluations against state-of-the-art methods show our approach excels in anonymization, attribute preservation, and image quality. Its flexibility, robustness, and practicality make it well-suited for real-world applications. Code and data can be found at https://github.com/hanweikung/nullface .

NullFace：无需训练的局部人脸匿名化技术

NullFace: Training-Free Localized Face Anonymization

摘要

Summary

Support