title: Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion
    Models
summary: Recent advancements in diffusion models revolutionize image generation but
  pose risks of misuse, such as replicating artworks or generating deepfakes.
  Existing image protection methods, though effective, struggle to balance
  protection efficacy, invisibility, and latency, thus limiting practical use. We
  introduce perturbation pre-training to reduce latency and propose a
  mixture-of-perturbations approach that dynamically adapts to input images to
  minimize performance degradation. Our novel training strategy computes
  protection loss across multiple VAE feature spaces, while adaptive targeted
  protection at inference enhances robustness and invisibility. Experiments show
  comparable protection performance with improved invisibility and drastically
  reduced inference time. The code and demo are available at
  https://webtoon.github.io/impasto
paper_authors._id: 67621bbacf56ce09ea4756d5
paper_authors.name: Namhyuk Ahn
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:36:57.224Z
paper_authors._id: 63686c355bb06007ea110ec9
paper_authors.avatarUrl: /avatars/902f4625b976bc5f734d6821402e9026.svg
paper_authors.fullname: Namhyuk Ahn
paper_authors.type: user
paper_authors.user: nmhkahn
paper_authors._id: 67621bbacf56ce09ea4756d6
paper_authors.name: KiYoon Yoo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:36:51.276Z
paper_authors._id: 62e88331dd52172c3896181c
paper_authors.avatarUrl: /avatars/c892d351437f680596f0ccfa63af2ebe.svg
paper_authors.fullname: KiYoon Yoo
paper_authors.type: user
paper_authors.user: kiyoonyoo
paper_authors._id: 67621bbacf56ce09ea4756d7
paper_authors.name: Wonhyuk Ahn
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:36:45.473Z
paper_authors._id: 64c35318aeb4eae54dc7973e
paper_authors.avatarUrl: /avatars/51352a300f432e72252d3551839c6b7f.svg
paper_authors.fullname: Wonhyuk Ahn
paper_authors.type: user
paper_authors.user: whahn
paper_authors._id: 67621bbacf56ce09ea4756d8
paper_authors.name: Daesik Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:36:39.657Z
paper_authors._id: 638fe9c639f7e2a7f9d2b868
paper_authors.avatarUrl: /avatars/7e67f59202ae66d054e3cd39526eb331.svg
paper_authors.fullname: Daesik Kim
paper_authors.type: user
paper_authors.user: daesik82
paper_authors._id: 67621bbacf56ce09ea4756d9
paper_authors.name: Seung-Hun Nam
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:36:26.120Z
paper_authors._id: 666a4f884649c647cf84aecd
paper_authors.avatarUrl: /avatars/cb6b8622fc38b370a33c6b68410c87e7.svg
paper_authors.fullname: Seung-Hun Nam
paper_authors.type: user
paper_authors.user: shnam
paper_title: Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion
    Models
paper_summary: Recent advancements in diffusion models revolutionize image generation but
  pose risks of misuse, such as replicating artworks or generating deepfakes.
  Existing image protection methods, though effective, struggle to balance
  protection efficacy, invisibility, and latency, thus limiting practical use. We
  introduce perturbation pre-training to reduce latency and propose a
  mixture-of-perturbations approach that dynamically adapts to input images to
  minimize performance degradation. Our novel training strategy computes
  protection loss across multiple VAE feature spaces, while adaptive targeted
  protection at inference enhances robustness and invisibility. Experiments show
  comparable protection performance with improved invisibility and drastically
  reduced inference time. The code and demo are available at
  https://webtoon.github.io/impasto
paper_ai_keywords: diffusion models image generation perturbation pre-training mixture-of-perturbations VAE feature spaces adaptive targeted protection