Toward effective protection against diffusion based mimicry through score distillation

Understand

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.