The proliferation of automated face recognition in the commercial and
government sectors has caused significant privacy concerns for individuals. One
approach to address these privacy concerns is to employ evasion attacks against
the metric embedding networks powering face recognition systems: Face
obfuscation systems generate imperceptibly perturbed images that cause face
recognition systems to misidentify the user. Perturbed faces are generated on
metric embedding networks, which are known to be unfair in the context of face
recognition. A question of demographic fairness naturally follows: are there
demographic disparities in face obfuscation system performance? We answer this
question with an analytical and empirical exploration of recent face
obfuscation systems. Metric embedding networks are found to be demographically
aware: face embeddings are clustered by demographic. We show how this
clustering behavior leads to reduced face obfuscation utility for faces in
minority groups. An intuitive analytical model yields insight into these
phenomena