Diffusion-based face swapping achieves state-of-the-art performance, yet it also exacerbates the potential harm of malicious face swapping to violate portraiture right or undermine personal reputation. This has spurred the development of proactive defense methods. However, existing approaches face a core trade-off: large perturbations distort facial structures, while small ones weaken protection effectiveness. To address these issues, we propose FaceDefense, an enhanced proactive defense framework against diffusion-based face swapping. Our method introduces a new diffusion loss to strengthen the defensive efficacy of adversarial examples, and employs a directional facial attribute editing to restore perturbation-induced distortions, thereby enhancing visual imperceptibility. A two-phase alternating optimization strategy is designed to generate final perturbed face images. Extensive experiments show that FaceDefense significantly outperforms existing methods in both imperceptibility and defense effectiveness, achieving a superior trade-off.

翻译：基于扩散模型的人脸交换技术虽已实现最先进的性能，却也加剧了恶意人脸交换侵犯肖像权或损害个人声誉的潜在危害。这推动了主动防御方法的发展。然而，现有方法面临一个核心权衡：大幅度的扰动会扭曲面部结构，而微小的扰动则会削弱保护效果。为解决这些问题，我们提出FaceDefense，一个针对扩散式人脸交换的增强型主动防御框架。我们的方法引入了一种新的扩散损失以增强对抗样本的防御效能，并采用方向性面部属性编辑来修复由扰动引起的畸变，从而提升视觉不可感知性。我们设计了一种两阶段交替优化策略来生成最终的扰动人脸图像。大量实验表明，FaceDefense在不可感知性和防御效能上均显著优于现有方法，实现了更优的权衡。