  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.
