Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective
This provides a robust privacy protection method for users sharing photos on social media, though it is incremental by building on existing adversarial perturbation techniques.
The paper tackles the problem of protecting personal photos from automatic recognition by introducing a game theory framework to optimize adversarial image perturbations, ensuring an upper bound on recognition rates regardless of countermeasures.
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.