通过级联路径破坏保障面部身份免受基于扩散的面部交换攻击
Safeguarding Facial Identity against Diffusion-based Face Swapping via Cascading Pathway Disruption
AI总结：
VoidFace通过级联路径破坏技术，有效防御基于扩散模型的面部交换攻击，提升身份安全性。
摘要：
扩散模型的快速演进使面部交换技术普及化，但也引发了隐私和身份安全的担忧。现有主动防御措施往往借鉴自图像编辑攻击，但在这种背景下效果不佳。我们归因于对面部交换系统中结构韧性以及独特的静态条件引导机制的忽视。为了解决这个问题，我们提出了VoidFace，一种系统性的防御方法，将面部交换视为耦合的身份路径。通过在关键瓶颈处注入扰动，VoidFace在整个管道中引发级联破坏。具体来说，我们首先引入定位破坏和身份擦除以破坏物理回归和语义嵌入，从而损害源面部的准确建模。然后我们通过解耦注意力机制干预生成域以切断身份注入，并通过破坏中间扩散特征以防止源身份的重建。为确保视觉不可察觉性，我们通过感知自适应策略在潜在空间中进行对抗搜索，以平衡攻击威力与图像质量。大量实验表明，VoidFace在各种基于扩散的交换模型上均优于现有防御措施，同时生成的对抗性面部具有更高质量的视觉效果。
英文摘要：
The rapid evolution of diffusion models has democratized face swapping but also raises concerns about privacy and identity security. Existing proactive defenses, often adapted from image editing attacks, prove ineffective in this context. We attribute this failure to an oversight of the structural resilience and the unique static conditional guidance mechanism inherent in face swapping systems. To address this, we propose VoidFace, a systemic defense method that views face swapping as a coupled identity pathway. By injecting perturbations at critical bottlenecks, VoidFace induces cascading disruption throughout the pipeline. Specifically, we first introduce localization disruption and identity erasure to degrade physical regression and semantic embeddings, thereby impairing the accurate modeling of the source face. We then intervene in the generative domain by decoupling attention mechanisms to sever identity injection, and corrupting intermediate diffusion features to prevent the reconstruction of source identity. To ensure visual imperceptibility, we perform adversarial search in the latent manifold, guided by a perceptual adaptive strategy to balance attack potency with image quality. Extensive experiments show that VoidFace outperforms existing defenses across various diffusion-based swapping models, while producing adversarial faces with superior visual quality.
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