  Deep learning-based systems have been shown to be vulnerable to adversarial
attacks in both digital and physical domains. While feasible, digital attacks
have limited applicability in attacking deployed systems, including face
recognition systems, where an adversary typically has access to the input and
not the transmission channel. In such setting, physical attacks that directly
provide a malicious input through the input channel pose a bigger threat. We
investigate the feasibility of conducting real-time physical attacks on face
recognition systems using adversarial light projections. A setup comprising a
commercially available web camera and a projector is used to conduct the
attack. The adversary uses a transformation-invariant adversarial pattern
generation method to generate a digital adversarial pattern using one or more
images of the target available to the adversary. The digital adversarial
pattern is then projected onto the adversary's face in the physical domain to
either impersonate a target (impersonation) or evade recognition (obfuscation).
We conduct preliminary experiments using two open-source and one commercial
face recognition system on a pool of 50 subjects. Our experimental results
demonstrate the vulnerability of face recognition systems to light projection
attacks in both white-box and black-box attack settings.
