  Advances in deep learning algorithms have enabled better-than-human
performance on face recognition tasks. In parallel, private companies have been
scraping social media and other public websites that tie photos to identities
and have built up large databases of labeled face images. Searches in these
databases are now being offered as a service to law enforcement and others and
carry a multitude of privacy risks for social media users. In this work, we
tackle the problem of providing privacy from such face recognition systems. We
propose and evaluate FoggySight, a solution that applies lessons learned from
the adversarial examples literature to modify facial photos in a
privacy-preserving manner before they are uploaded to social media.
FoggySight's core feature is a community protection strategy where users acting
as protectors of privacy for others upload decoy photos generated by
adversarial machine learning algorithms. We explore different settings for this
scheme and find that it does enable protection of facial privacy -- including
against a facial recognition service with unknown internals.
