NeRFTAP: Enhancing Transferability of Adversarial Patches on Face
  Recognition using Neural Radiance Fields
Xiaoliang Liu Furao Shen Feng Han Jian Zhao Changhai Nie
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.