English

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study

Computer Vision and Pattern Recognition 2025-07-08 v1 Artificial Intelligence

Abstract

With the increasing adoption of diffusion models for image generation and personalization, concerns regarding privacy breaches and content misuse have become more pressing. In this study, we conduct a comprehensive comparison of eight perturbation based protection methods: AdvDM, ASPL, FSGM, MetaCloak, Mist, PhotoGuard, SDS, and SimAC--across both portrait and artwork domains. These methods are evaluated under varying perturbation budgets, using a range of metrics to assess visual imperceptibility and protective efficacy. Our results offer practical guidance for method selection. Code is available at: https://github.com/vkeilo/DiffAdvPerturbationBench.

Keywords

Cite

@article{arxiv.2507.03953,
  title  = {Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study},
  author = {Kai Ye and Tianyi Chen and Zhen Wang},
  journal= {arXiv preprint arXiv:2507.03953},
  year   = {2025}
}

Comments

Accepted to the 2nd Workshop on Reliable and Responsible Foundation Models (R2-FM 2025) at ICML. 8 pages, 3 figures

R2 v1 2026-07-01T03:47:32.803Z