AEGIS：通过对抗扰动保护3D人脸虚拟形象的隐私
AEGIS: Preserving privacy of 3D Facial Avatars with Adversarial Perturbations
AI总结：
AEGIS通过对抗扰动技术，为3D人脸虚拟形象提供隐私保护，有效隐藏身份特征同时保持真实感和功能性。
摘要：
随着逼真3D人脸虚拟形象的广泛应用，特别是采用高效的3D高斯点散布表示法的虚拟形象，带来了新的在线身份盗用风险，尤其是在依赖生物识别认证的系统中。尽管已开发出有效的对抗掩蔽方法用于2D图像，但在实现对动态3D虚拟形象的鲁棒且视角一致的身份保护方面仍存在显著差距。为此，我们提出了AEGIS，这是首个针对3D高斯虚拟形象的隐私保护身份掩蔽框架，旨在保持主体的感知特征。我们的方法旨在隐藏与身份相关的人脸特征，同时保持虚拟形象的感知真实性和功能性完整性。AEGIS通过预训练的面部验证网络指导，对高斯颜色系数应用对抗扰动，确保在多个视角下一致的保护，无需重新训练或修改虚拟形象的几何结构。AEGIS实现了完全去标识化，将面部检索和验证准确率降至0%，同时保持高感知质量（SSIM=0.9555，PSNR=35.52 dB）。它还保留了关键面部属性，如年龄、种族、性别和情绪，展示了强隐私保护，且视觉失真最小。
英文摘要：
The growing adoption of photorealistic 3D facial avatars, particularly those utilizing efficient 3D Gaussian Splatting representations, introduces new risks of online identity theft, especially in systems that rely on biometric authentication. While effective adversarial masking methods have been developed for 2D images, a significant gap remains in achieving robust, viewpoint-consistent identity protection for dynamic 3D avatars. To address this, we present AEGIS, the first privacy-preserving identity masking framework for 3D Gaussian Avatars that maintains the subject's perceived characteristics. Our method aims to conceal identity-related facial features while preserving the avatar's perceptual realism and functional integrity. AEGIS applies adversarial perturbations to the Gaussian color coefficients, guided by a pre-trained face verification network, ensuring consistent protection across multiple viewpoints without retraining or modifying the avatar's geometry. AEGIS achieves complete de-identification, reducing face retrieval and verification accuracy to 0%, while maintaining high perceptual quality (SSIM = 0.9555, PSNR = 35.52 dB). It also preserves key facial attributes such as age, race, gender, and emotion, demonstrating strong privacy protection with minimal visual distortion.
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