Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models
Namhyuk Ahn, KiYoon Yoo, Wonhyuk Ahn, Daesik Kim, Seung-Hun Nam
2024-12-17
Summary
This paper discusses a method called Nearly Zero-Cost Protection, which uses personalized diffusion models to protect images from being copied or misused while maintaining their quality.
What's the problem?
As image generation technologies improve, there are increasing concerns about misuse, such as creating fake images or replicating existing artworks without permission. Current methods for protecting images often struggle to balance how well they protect the images, how invisible those protections are, and how quickly they work. This makes it difficult to use these protection methods in real-world applications.
What's the solution?
The authors propose a new approach that involves using perturbation pre-training to speed up the process of protecting images. They introduce a mixture-of-perturbations technique that adapts to different input images, ensuring that the protection is effective without degrading the image quality. Their training strategy focuses on minimizing the loss of important features while enhancing the invisibility of the protection during image generation. The results show that this method provides strong protection against copying while being less noticeable and faster than previous methods.
Why it matters?
This research is important because it addresses the growing need for effective image protection in a world where digital content can be easily replicated and misused. By improving how we can protect images without sacrificing their quality, this method can help artists and creators safeguard their work from unauthorized use, ensuring that their rights are respected in the digital space.
Abstract
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