Edit Away and My Face Will not Stay: Personal Biometric Defense against
  Malicious Generative Editing
Authors: name: Hanhui Wang, name: Yihua Zhang, name: Ruizheng Bai, name: Yue Zhao, name: Sijia Liu, name: Zhengzhong Tu

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.