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.

翻译：人脸识别系统的广泛部署，使得社交媒体和公共平台上分享的个人图像面临身份关联和隐私泄露风险。现有对抗性隐私保护方法虽可削弱未授权人脸识别系统的性能，但与生成式人脸编辑不兼容。人工智能驱动的人脸编辑工具日益普及，显著提升了用户对个性化肖像生成和社交分享的需求。然而，当前编辑方法通常保留身份特征，导致编辑后图像仍易被恶意人脸识别系统追踪。为此，本文提出Flux-Guard——一种基于对抗攻击的隐私保护人脸编辑框架，该框架将人脸编辑与隐私保护整合于统一生成过程中。具体而言，我们设计了流轨迹控制方法以对齐语义操作与生成过程，并引入具有自适应感知损失驱动加权策略的隐空间对抗优化，通过动态调整对抗强度在保持视觉质量的同时最大化攻击效能。大量实验表明，Flux-Guard在支持人脸编辑的同时，显著提升了针对CelebA-HQ和LADN数据集上跨域人脸识别模型的攻击成功率。此外，商业API评估结果验证了其在真实应用中的有效性。代码已发布于https://github.com/JLMWang/Flux-Guard。