title: Face Anonymization Made Simple
summary: 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 .
paper_authors._id: 6728d794fd85cbe3de7d6491
paper_authors.name: Han-Wei Kung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-04T21:24:15.318Z
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: 6728d794fd85cbe3de7d6492
paper_authors.name: Tuomas Varanka
paper_authors._id: 6728d794fd85cbe3de7d6493
paper_authors.name: Sanjay Saha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-04T21:33:27.448Z
paper_authors._id: 636f4efc5af6ffb656353f4f
paper_authors.avatarUrl: /avatars/0ada75b00e4045645e0cfaa31acfbc25.svg
paper_authors.fullname: Sanjay Saha
paper_authors.type: user
paper_authors.user: sanjaysaha
paper_authors._id: 6728d794fd85cbe3de7d6494
paper_authors.name: Terence Sim
paper_authors._id: 6728d794fd85cbe3de7d6495
paper_authors.name: Nicu Sebe
paper_title: Face Anonymization Made Simple
paper_summary: 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 .
paper_ai_keywords: diffusion models reconstruction loss face recognition models identity anonymization facial attribute preservation image quality face swapping