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arXiv · 2507.03953

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study

Kai Ye·Tianyi Chen·Zhen Wang
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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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Kai Ye, Tianyi Chen, Zhen Wang. 2025-07-05. Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study. https://arxiv.org/abs/2507.03953

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