  Users like sharing personal photos with others through social media. At the
same time, they might want to make automatic identification in such photos
difficult or even impossible. Classic obfuscation methods such as blurring are
not only unpleasant but also not as effective as one would expect. Recent
studies on adversarial image perturbations (AIP) suggest that it is possible to
confuse recognition systems effectively without unpleasant artifacts. However,
in the presence of counter measures against AIPs, it is unclear how effective
AIP would be in particular when the choice of counter measure is unknown. Game
theory provides tools for studying the interaction between agents with
uncertainties in the strategies. We introduce a general game theoretical
framework for the user-recogniser dynamics, and present a case study that
involves current state of the art AIP and person recognition techniques. We
derive the optimal strategy for the user that assures an upper bound on the
recognition rate independent of the recogniser's counter measure. Code is
available at https://goo.gl/hgvbNK.
