Flux-Guard：使用扩散模型的面部身份保护
Flux-Guard: Facial Identity Protection using diffusion models
AI总结：
提出Flux-Guard框架，通过流轨迹控制和潜在空间对抗优化，在统一生成过程中实现面部编辑与隐私保护，有效提升对跨域人脸识别模型的攻击成功率。
摘要：
人脸识别系统的广泛部署使得社交媒体和公共平台上共享的个人图像面临身份关联和隐私风险。现有的对抗性隐私保护方法可以降低未经授权的人脸识别性能，但与生成式面部编辑不兼容。人工智能驱动的面部编辑工具越来越受欢迎，这显著增加了用户对个性化肖像生成和社交分享的需求。然而，当前的编辑方法通常保留身份特征，使得编辑后的图像仍然容易被恶意人脸识别系统追踪。因此，本文提出了Flux-Guard，一种基于对抗攻击的隐私保护面部编辑框架，它在统一的生成过程中集成了面部编辑和隐私保护。具体地，我们设计了一种流轨迹控制方法，将语义操作与生成过程对齐，并引入了潜在空间对抗优化，采用自适应感知损失驱动的加权策略，动态调整对抗强度以在保持视觉质量的同时最大化攻击效果。大量实验表明，Flux-Guard支持面部编辑，同时在CelebA-HQ和LADN数据集上显著提高了对跨域人脸识别模型的攻击成功率。此外，对商业API的评估结果证实了其在现实世界应用中的有效性。代码发布在https://this URL。
英文摘要：
The widespread deployment of face recognition (FR) systems exposes personal images shared on social media and public platforms to identity linkage and privacy risks. Existing adversarial privacy protection methods can degrade unauthorized FR performance but are not compatible with generative face editing. Artificial intelligence-driven face editing tools are gaining popularity, which has significantly increased user demand for personalized portrait generation and social sharing. However, current editing methods often preserve identity features, making the edited images still susceptible to tracking by malicious FR systems. Thus, this paper proposes Flux-Guard, a privacy-preserving face editing framework based on adversarial attacks, which integrates face editing and privacy protection within a unified generative process. Specifically, we design a flow trajectory control method to align semantic manipulations with the generative process and introduce latent-space adversarial optimization with an adaptive perceptual-loss-driven weighting strategy, dynamically adjusting adversarial strength to maximize attack effectiveness while preserving visual quality. Extensive experiments demonstrate that Flux-Guard supports face editing while significantly improving attack success rates against cross-domain face recognition models on the CelebA-HQ and LADN datasets. Furthermore, evaluation results for commercial APIs have confirmed its effectiveness in real-world applications. The code is released at https://github.com/JLMWang/Flux-Guard.
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。
