Can GAN-induced Attribute Manipulations Impact Face Recognition?
This addresses the vulnerability of automated face recognition systems to digital attribute modifications, which is an incremental but important security concern.
The study investigated how GAN-induced attribute manipulations, such as adding eyeglasses or altering sex cues, affect face recognition performance, finding that some modifications can impair verification accuracy by up to 73%.
Impact due to demographic factors such as age, sex, race, etc., has been studied extensively in automated face recognition systems. However, the impact of \textit{digitally modified} demographic and facial attributes on face recognition is relatively under-explored. In this work, we study the effect of attribute manipulations induced via generative adversarial networks (GANs) on face recognition performance. We conduct experiments on the CelebA dataset by intentionally modifying thirteen attributes using AttGAN and STGAN and evaluating their impact on two deep learning-based face verification methods, ArcFace and VGGFace. Our findings indicate that some attribute manipulations involving eyeglasses and digital alteration of sex cues can significantly impair face recognition by up to 73% and need further analysis.