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Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons134225

Oh,  Seong Joon
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1703.09471.pdf
(Preprint), 5MB

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Citation

Oh, S. J., Fritz, M., & Schiele, B. (2017). Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective. Retrieved from http://arxiv.org/abs/1703.09471.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002D-7CAE-2
Abstract
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.