Face recognition (FR) technology plays a crucial role in various
applications, but its vulnerability to adversarial attacks poses significant
security concerns. Existing research primarily focuses on transferability to
different FR models, overlooking the direct transferability to victim's face
images, which is a practical threat in real-world scenarios. In this study, we
propose a novel adversarial attack method that considers both the
transferability to the FR model and the victim's face image, called NeRFTAP.
Leveraging NeRF-based 3D-GAN, we generate new view face images for the source
and target subjects to enhance transferability of adversarial patches. We
introduce a style consistency loss to ensure the visual similarity between the
adversarial UV map and the target UV map under a 0-1 mask, enhancing the
effectiveness and naturalness of the generated adversarial face images.
Extensive experiments and evaluations on various FR models demonstrate the
superiority of our approach over existing attack techniques. Our work provides
valuable insights for enhancing the robustness of FR systems in practical
adversarial settings.

我们提出了一种考虑到人脸识别模型的可迁移性和受害者人脸图像的新型对抗攻击方法 NeRFTAP，在使用 NeRF-based 3D-GAN 生成新视角人脸图像的基础上，引入了样式一致性损失来提高生成的对抗性人脸图像的有效性和自然性。在各种人脸识别模型上的大量实验证明我们的方法相比现有的攻击技术更为优越。我们的研究为提高实际对抗环境中人脸识别系统的稳健性提供了宝贵的见解。