  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.
