Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models

Namhyuk Ahn, KiYoon Yoo, Wonhyuk Ahn, Daesik Kim, Seung-Hun Nam

Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models: 2 upvotes on Hugging Face Daily Papers, #27 of 28 papers on 2024-12-17. Day-by-day upvote history.

Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce perturbation pre-training to reduce latency and propose a mixture-of-perturbations approach that dynamically adapts to input images to minimize performance degradation. Our novel training strategy computes protection loss across multiple VAE feature spaces, while adaptive targeted protection at inference enhances robustness and invisibility. Experiments show comparable protection performance with improved invisibility and drastically reduced inference time. The code and demo are available at https://webtoon.github.io/impasto

Paper page on Hugging Face · arXiv

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