Mitigating the Impact of Attribute Editing on Face Recognition
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909164817612800 |
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| author | Banerjee, Sudipta Mullangi, Sai Pranaswi Wagle, Shruti Hegde, Chinmay Memon, Nasir |
| author_facet | Banerjee, Sudipta Mullangi, Sai Pranaswi Wagle, Shruti Hegde, Chinmay Memon, Nasir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating the Impact of Attribute Editing on Face Recognition Banerjee, Sudipta Mullangi, Sai Pranaswi Wagle, Shruti Hegde, Chinmay Memon, Nasir Computer Vision and Pattern 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. |
| title | Mitigating the Impact of Attribute Editing on Face Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.08092 |