  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.
