FreqPure: A High-Frequency Preservation Diffusion-Based Purification Method for Protective Perturbation Removal

Diffusion models can be used to generate highly realistic likenesses of individuals when fine-tuned on a few personal images, raising serious concerns about unauthorized use and identity exploitation. In response, proactive defenses have been proposed by incorporating imperceptible protective perturbations into images to prevent unauthorized fine-tuning. Although these techniques are promising, their robustness under real-world conditions, such as common post-processing and adversarial purification, remains largely unexplored. In this work, we first evaluate the robustness of state-of-the-art protective methods with various post-processing and purification techniques. Moreover, to address the limitations of existing purification methods of removing protective perturbations along with natural high-frequency components, we propose FreqPure, a high-frequency-aware, diffusion-based purification approach that incorporates frequency consistency constraint to better preserve image fidelity during purification. It integrates a two-stage purification pipeline: 1) A reconstruction module that removes artifacts introduced by protection techniques. 2) A diffusion-based model that synthesizes high-frequency components, ensuring the output remains realistic. Extensive experiment results show that the proposed approach can purify perturbation on the image efficiently while preserving the natural high-frequency details of the images.