Faceless Person Recognition: Privacy Implications in Social Media
As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy. This works contributes to the understanding of privacy implications of such data sharing by analysing how we
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Abstract. As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy. This works contributes to the understanding of privacy implications of such data sharing by analysing how well people are recognisable in social media data. To facilitate a systematic study we define a number of scenarios considering factors such as how many heads of a person are tagged and if those heads are obfuscated or not. We propose a robust person recognition system that can handle large variations in pose and clothing, and can be trained with few training samples. Our results indicate that a handful of images is enough to threaten users’ privacy, even in the presence of obfuscation. We show detailed experimental results, and discuss their implications. Keywords: Privacy
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· Person recognition · Social media
Introduction
With the growth of the internet, more and more people share and disseminate large amounts of personal data be it on webpages, in social networks, or through personal communication. The steadily growing computation power, advances in
Fig. 1. An illustration of one of the scenarios considered: can a vision system recognise that the person in the right image is the same as the tagged person in the left images, even when the head is obfuscated? Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46487-9 2) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part III, LNCS 9907, pp. 19–35, 2016. DOI: 10.1007/978-3-319-46487-9 2
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machine learning, and the growth of the internet economy, have created strong revenue streams and a thriving industry built on monetising user data. It is clear that visual data contains private information, yet the privacy implications of this data dissemination are unclear, even for computer vision experts. We are aiming for a transparent and quantifiable understanding of the loss in privacy incurred by sharing personal data online, both for the uploader and other users who appear in the data. In this work, we investigate the privacy implications of disseminating photos of people through social media. Although social media data allows to identify a person via different data types (timeline, geolocation, language, user profile, etc.) [1], we focus on the pixel content of an image. We want to know how well a vision system can recognise a person in social photos (using the image content only), and how well users can control their privacy when limiting the number of tagged images or when adding varying degrees of obfuscation (see Fig. 1) to their heads. An important component to extract maximal information out of visual data in social networks is to fuse different data and provide a joint analysis. We propose our new Faceless Person Recogniser (described in Sect. 5), which not only reasons about individual images, but uses graph inference to deduce identities
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