Imperceptible Protection against Style Imitation from Diffusion Models
Namhyuk Ahn Wonhyuk Ahn KiYoon Yoo Daesik Kim Seung-Hun Nam
Recent progress in diffusion models has profoundly enhanced the fidelity of
image generation. However, this has raised concerns about copyright
infringements. While prior methods have introduced adversarial perturbations to
prevent style imitation, most are accompanied by the degradation of artworks'
visual quality. Recognizing the importance of maintaining this, we develop a
visually improved protection method that preserves its protection capability.
To this end, we create a perceptual map to identify areas most sensitive to
human eyes. We then adjust the protection intensity guided by an instance-aware
refinement. We also integrate a perceptual constraints bank to further improve
the imperceptibility. Results show that our method substantially elevates the
quality of the protected image without compromising on protection efficacy.