  While generative diffusion models excel in producing high-quality images,
they can also be misused to mimic authorized images, posing a significant
threat to AI systems. Efforts have been made to add calibrated perturbations to
protect images from diffusion-based mimicry pipelines. However, most of the
existing methods are too ineffective and even impractical to be used by
individual users due to their high computation and memory requirements. In this
work, we present novel findings on attacking latent diffusion models (LDM) and
propose new plug-and-play strategies for more effective protection. In
particular, we explore the bottleneck in attacking an LDM, discovering that the
encoder module rather than the denoiser module is the vulnerable point. Based
on this insight, we present our strategy using Score Distillation Sampling
(SDS) to double the speed of protection and reduce memory occupation by half
without compromising its strength. Additionally, we provide a robust protection
strategy by counterintuitively minimizing the semantic loss, which can assist
in generating more natural perturbations. Finally, we conduct extensive
experiments to substantiate our findings and comprehensively evaluate our newly
proposed strategies. We hope our insights and protective measures can
contribute to better defense against malicious diffusion-based mimicry,
advancing the development of secure AI systems. The code is available in
https://github.com/xavihart/Diff-Protect
