我的脸是我的，不是你的：针对扩散模型换脸的面部保护
My Face Is Mine, Not Yours: Facial Protection Against Diffusion Model Face Swapping
AI总结：
本文提出一种针对扩散模型换脸的主动防御策略，通过对抗性攻击预防性保护面部图像，克服现有方法依赖特定架构和全局扰动的局限。
摘要：
基于扩散的深度伪造技术的普及对未经授权和不道德的面部图像操纵构成了重大风险。虽然传统的对抗措施主要集中于被动检测方法，但本文通过对抗性攻击引入了一种新颖的主动防御策略，该策略可预防性地保护面部图像免遭基于扩散的深度伪造系统的利用。现有的对抗性保护方法主要针对传统的生成架构（GAN、AE、VAE），未能解决扩散模型带来的独特挑战，而扩散模型已成为高质量面部深度伪造的主要框架。当前针对扩散模型的对抗性方法受限于其对特定模型架构和权重的依赖，使其在面对多样化的基于扩散的深度伪造实现时效果不佳。此外，它们通常采用全局扰动策略，未能充分处理深度伪造中面部操纵的区域特定性质。
英文摘要：
The proliferation of diffusion-based deepfake technologies poses significant risks for unauthorized and unethical facial image manipulation. While traditional countermeasures have primarily focused on passive detection methods, this paper introduces a novel proactive defense strategy through adversarial attacks that preemptively protect facial images from being exploited by diffusion-based deepfake systems. Existing adversarial protection methods predominantly target conventional generative architectures (GANs, AEs, VAEs) and fail to address the unique challenges presented by diffusion models, which have become the predominant framework for high-quality facial deepfakes. Current diffusion-specific adversarial approaches are limited by their reliance on specific model architectures and weights, rendering them ineffective against the diverse landscape of diffusion-based deepfake implementations. Additionally, they typically employ global perturbation strategies that inadequately address the region-specific nature of facial manipulation in deepfakes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
