  Facial recognition tools are becoming exceptionally accurate in identifying
people from images. However, this comes at the cost of privacy for users of
online services with photo management (e.g. social media platforms).
Particularly troubling is the ability to leverage unsupervised learning to
recognize faces even when the user has not labeled their images. In this paper
we propose Ulixes, a strategy to generate visually non-invasive facial noise
masks that yield adversarial examples, preventing the formation of identifiable
user clusters in the embedding space of facial encoders. This is applicable
even when a user is unmasked and labeled images are available online. We
demonstrate the effectiveness of Ulixes by showing that various classification
and clustering methods cannot reliably label the adversarial examples we
generate. We also study the effects of Ulixes in various black-box settings and
compare it to the current state of the art in adversarial machine learning.
Finally, we challenge the effectiveness of Ulixes against adversarially trained
models and show that it is robust to countermeasures.
