Mitigating the Impact of Attribute Editing on Face Recognition

• URL: http://arxiv.org/abs/2403.08092v2
• Date: Tue, 9 Apr 2024 20:55:01 GMT
• Title: Mitigating the Impact of Attribute Editing on Face Recognition
• Authors: Sudipta Banerjee, Sai Pranaswi Mullangi, Shruti Wagle, Chinmay Hegde, Nasir Memon,
• Abstract summary: We show that facial attribute editing using modern generative AI models can severely degrade automated face recognition systems. We propose two novel techniques for local and global attribute editing.
• Score: 14.138965856511387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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