用个性化扩散模型实现几乎零成本的图像仿冒防护
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models
- 通过扰动预训练降低推理延迟，动态适配输入图像
- 在多个VAE特征空间计算保护损失，提升隐蔽性与鲁棒性
- 适合需要快速部署且对隐蔽性要求高的生成式应用
扩散模型虽推动了图像生成的发展，但存在艺术作品复制和深度伪造等滥用风险。现有图像保护方法难以兼顾保护效果、隐蔽性和延迟，限制了实际应用。本文提出扰动预训练以降低延迟，并采用混合扰动策略，动态适应输入图像以最小化性能下降。创新性训练策略在多个VAE特征空间中计算保护损失，推理时自适应目标保护进一步增强鲁棒性与隐蔽性。实验表明，保护效果相当，但隐蔽性显著提升，推理时间大幅缩短。代码与演示见 https://webtoon.github.io/impasto
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。