title: NullFace: Training-Free Localized Face Anonymization
summary: 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 .
paper_authors._id: 67d12d0b44be28339053b965
paper_authors.name: Han-Wei Kung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T08:50:08.946Z
paper_authors._id: 64a3eb280111d5ff6c4849fd
paper_authors.avatarUrl: /avatars/3a9000393b8d200418bae5fe7d902e4d.svg
paper_authors.fullname: Han-Wei Kung
paper_authors.type: user
paper_authors.user: hkung
paper_authors._id: 67d12d0b44be28339053b966
paper_authors.name: Tuomas Varanka
paper_authors._id: 67d12d0b44be28339053b967
paper_authors.name: Terence Sim
paper_authors._id: 67d12d0b44be28339053b968
paper_authors.name: Nicu Sebe
paper_title: NullFace: Training-Free Localized Face Anonymization
paper_summary: 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 .
paper_ai_summary: A pre-trained text-to-image diffusion model is employed for face anonymization, preserving attributes while ensuring privacy without requiring optimization or training.
paper_ai_keywords: diffusion model text-to-image face anonymization identity embedding denoising identity-conditioned