Edit Away and My Face Will not Stay: Personal Biometric Defense against
  Malicious Generative Editing
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.
67480ee880aebd7436eae509
Hanhui Wang
67480ee880aebd7436eae50a
Yihua Zhang
67480ee880aebd7436eae50b
Ruizheng Bai
67480ee880aebd7436eae50c
Yue Zhao
67480ee880aebd7436eae50d
Sijia Liu
67480ee880aebd7436eae50e
Zhengzhong Tu
claimed_verified
2025-06-03T09:16:06.363Z
62548d5fef3debb2ddf91217
/avatars/14975b45568f9c399c92c3986b6ce83e.svg
Zhengzhong Tu
user
vztu
Edit Away and My Face Will not Stay: Personal Biometric Defense against
  Malicious Generative Editing
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.
FaceLock integrates facial recognition and visual perception to optimize adversarial perturbations, enhancing protection against malicious edits in generative image editing.
diffusion models generative image editing adversarial perturbations biometric information facial recognition visual perception biometric defense privacy-preserving practices image editing purification techniques ablation studies