Mitigating the Impact of Attribute Editing on Face Recognition
<|reference_start|>Mitigating the Impact of Attribute Editing on Face Recognition: Through a large-scale study over diverse face images, we show that facial attribute editing using modern generative AI models can severely degrade automated face recognition systems. This degradation persists even with identity-preserving generative models. To mitigate this issue, we propose two novel techniques for local and global attribute editing. We empirically ablate twenty-six facial semantic, demographic and expression-based attributes that have been edited using state-of-the-art generative models, and evaluate them using ArcFace and AdaFace matchers on CelebA, CelebAMaskHQ and LFW datasets. Finally, we use LLaVA, an emerging visual question-answering framework for attribute prediction to validate our editing techniques. Our methods outperform the current state-of-the-art at facial editing (BLIP, InstantID) while improving identity retention by a significant extent.<|reference_end|>
@article{banerjee2024mitigating,
    title={Mitigating the Impact of Attribute Editing on Face Recognition},
    author={Sudipta Banerjee, Sai Pranaswi Mullangi, Shruti Wagle, Chinmay Hegde,
  Nasir Memon},
    journal={arXiv preprint arXiv:2403.08092},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08092},
    primaryClass={cs.CV}
}