Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?
This addresses privacy and copyright issues for users of generative AI models, but it is incremental as it builds on existing perturbation methods.
The paper tackled the problem of using protective perturbations to safeguard personal data from exploitation by Stable Diffusion, finding that these approaches are insufficient in practical scenarios and introducing a purification method that removes perturbations, allowing Stable Diffusion to learn effectively from purified images.
Stable Diffusion has established itself as a foundation model in generative AI artistic applications, receiving widespread research and application. Some recent fine-tuning methods have made it feasible for individuals to implant personalized concepts onto the basic Stable Diffusion model with minimal computational costs on small datasets. However, these innovations have also given rise to issues like facial privacy forgery and artistic copyright infringement. In recent studies, researchers have explored the addition of imperceptible adversarial perturbations to images to prevent potential unauthorized exploitation and infringements when personal data is used for fine-tuning Stable Diffusion. Although these studies have demonstrated the ability to protect images, it is essential to consider that these methods may not be entirely applicable in real-world scenarios. In this paper, we systematically evaluate the use of perturbations to protect images within a practical threat model. The results suggest that these approaches may not be sufficient to safeguard image privacy and copyright effectively. Furthermore, we introduce a purification method capable of removing protected perturbations while preserving the original image structure to the greatest extent possible. Experiments reveal that Stable Diffusion can effectively learn from purified images over all protective methods.