NullFace: Training-Free Localized Face Anonymization
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 .
67d12d0b44be28339053b965
Han-Wei Kung
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2025-03-12T08:50:08.946Z
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Han-Wei Kung
user
hkung
67d12d0b44be28339053b966
Tuomas Varanka
67d12d0b44be28339053b967
Terence Sim
67d12d0b44be28339053b968
Nicu Sebe
NullFace: Training-Free Localized Face Anonymization
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 .
A pre-trained text-to-image diffusion model is employed for face anonymization, preserving attributes while ensuring privacy without requiring optimization or training.
diffusion model text-to-image face anonymization identity embedding denoising identity-conditioned