title: Edit Away and My Face Will not Stay: Personal Biometric Defense against
    Malicious Generative Editing
summary: Recent advancements in diffusion models have made generative image editing
  more accessible, enabling creative edits but raising ethical concerns,
  particularly regarding malicious edits to human portraits that threaten privacy
  and identity security. Existing protection methods primarily rely on
  adversarial perturbations to nullify edits but often fail against diverse
  editing requests. We propose FaceLock, a novel approach to portrait protection
  that optimizes adversarial perturbations to destroy or significantly alter
  biometric information, rendering edited outputs biometrically unrecognizable.
  FaceLock integrates facial recognition and visual perception into perturbation
  optimization to provide robust protection against various editing attempts. We
  also highlight flaws in commonly used evaluation metrics and reveal how they
  can be manipulated, emphasizing the need for reliable assessments of
  protection. Experiments show FaceLock outperforms baselines in defending
  against malicious edits and is robust against purification techniques. Ablation
  studies confirm its stability and broad applicability across diffusion-based
  editing algorithms. Our work advances biometric defense and sets the foundation
  for privacy-preserving practices in image editing. The code is available at:
  https://github.com/taco-group/FaceLock.
paper_authors._id: 67480ee880aebd7436eae509
paper_authors.name: Hanhui Wang
paper_authors._id: 67480ee880aebd7436eae50a
paper_authors.name: Yihua Zhang
paper_authors._id: 67480ee880aebd7436eae50b
paper_authors.name: Ruizheng Bai
paper_authors._id: 67480ee880aebd7436eae50c
paper_authors.name: Yue Zhao
paper_authors._id: 67480ee880aebd7436eae50d
paper_authors.name: Sijia Liu
paper_authors._id: 67480ee880aebd7436eae50e
paper_authors.name: Zhengzhong Tu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T09:16:06.363Z
paper_authors._id: 62548d5fef3debb2ddf91217
paper_authors.avatarUrl: /avatars/14975b45568f9c399c92c3986b6ce83e.svg
paper_authors.fullname: Zhengzhong Tu
paper_authors.type: user
paper_authors.user: vztu
paper_title: Edit Away and My Face Will not Stay: Personal Biometric Defense against
    Malicious Generative Editing
paper_summary: Recent advancements in diffusion models have made generative image editing
  more accessible, enabling creative edits but raising ethical concerns,
  particularly regarding malicious edits to human portraits that threaten privacy
  and identity security. Existing protection methods primarily rely on
  adversarial perturbations to nullify edits but often fail against diverse
  editing requests. We propose FaceLock, a novel approach to portrait protection
  that optimizes adversarial perturbations to destroy or significantly alter
  biometric information, rendering edited outputs biometrically unrecognizable.
  FaceLock integrates facial recognition and visual perception into perturbation
  optimization to provide robust protection against various editing attempts. We
  also highlight flaws in commonly used evaluation metrics and reveal how they
  can be manipulated, emphasizing the need for reliable assessments of
  protection. Experiments show FaceLock outperforms baselines in defending
  against malicious edits and is robust against purification techniques. Ablation
  studies confirm its stability and broad applicability across diffusion-based
  editing algorithms. Our work advances biometric defense and sets the foundation
  for privacy-preserving practices in image editing. The code is available at:
  https://github.com/taco-group/FaceLock.
paper_ai_summary: FaceLock integrates facial recognition and visual perception to optimize adversarial perturbations, enhancing protection against malicious edits in generative image editing.
paper_ai_keywords: diffusion models generative image editing adversarial perturbations biometric information facial recognition visual perception biometric defense privacy-preserving practices image editing purification techniques ablation studies