Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study
Kai Ye · Tianyi Chen · Zhen Wang
Keywords:
Diffusion Models
Privacy Protection
Personalized Image Generation
Adversarial Perturbations
Robustness Evaluation
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.
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