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Face recognition (FR) technology plays a crucial role in variousapplications, but its vulnerability to adversarial attacks poses significantsecurity concerns. Existing research primarily focuses on transferability todifferent FR models, overlooking the direct transferability to victim's faceimages, which is a practical threat in real-world scenarios. In this study, wepropose a novel adversarial attack method that considers both thetransferability 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 sourceand target subjects to enhance transferability of adversarial patches. Weintroduce a style consistency loss to ensure the visual similarity between theadversarial UV map and the target UV map under a 0-1 mask, enhancing theeffectiveness and naturalness of the generated adversarial face images.Extensive experiments and evaluations on various FR models demonstrate thesuperiority of our approach over existing attack techniques. Our work providesvaluable insights for enhancing the robustness of FR systems in practicaladversarial settings.

NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields