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Personal photos of individuals when shared online, apart from exhibiting amyriad of memorable details, also reveals a wide range of private informationand potentially entails privacy risks (e.g., online harassment, tracking). Tomitigate such risks, it is crucial to study techniques that allow individualsto limit the private information leaked in visual data. We tackle this problemin a novel image obfuscation framework: to maximize entropy on inferences overtargeted privacy attributes, while retaining image fidelity. We approach theproblem based on an encoder-decoder style architecture, with two key novelties:(a) introducing a discriminator to perform bi-directional translationsimultaneously from multiple unpaired domains; (b) predicting an imageinterpolation which maximizes uncertainty over a target set of attributes. Wefind our approach generates obfuscated images faithful to the original inputimages, and additionally increase uncertainty by 6.2$\times$ (or up to 0.85bits) over the non-obfuscated counterparts.

InfoScrub: Towards Attribute Privacy by Targeted Obfuscation