CamForensics: Understanding Visual Privacy Leaks in the Wild

Animesh Srivastava, Puneet Jain, Soteris Demetriou, Landon P. Cox, Kyu-Han Kim
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引用次数: 10

Abstract

Many mobile apps, including augmented-reality games, bar-code readers, and document scanners, digitize information from the physical world by applying computer-vision algorithms to live camera data. However, because camera permissions for existing mobile operating systems are coarse (i.e., an app may access a camera's entire view or none of it), users are vulnerable to visual privacy leaks. An app violates visual privacy if it extracts information from camera data in unexpected ways. For example, a user might be surprised to find that an augmented-reality makeup app extracts text from the camera's view in addition to detecting faces. This paper presents results from the first large-scale study of visual privacy leaks in the wild. We build CamForensics to identify the kind of information that apps extract from camera data. Our extensive user surveys determine what kind of information users expected an app to extract. Finally, our results show that camera apps frequently defy users' expectations based on their descriptions.
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CamForensics:了解野外视觉隐私泄露
许多移动应用程序,包括增强现实游戏、条形码阅读器和文档扫描仪,通过将计算机视觉算法应用于实时摄像机数据,将来自物理世界的信息数字化。然而,由于现有移动操作系统的摄像头权限很粗糙(例如,应用程序可以访问摄像头的整个视图,也可以不访问),用户很容易受到视觉隐私泄露的影响。如果应用程序以意想不到的方式从摄像头数据中提取信息,就会侵犯视觉隐私。例如,用户可能会惊讶地发现,除了检测人脸之外,增强现实化妆应用程序还可以从相机视图中提取文本。本文介绍了首次大规模野外视觉隐私泄露研究的结果。我们建立CamForensics来识别应用程序从摄像头数据中提取的信息。我们广泛的用户调查决定了用户希望应用程序提取什么样的信息。最后,我们的研究结果表明,相机应用程序经常违背用户对其描述的期望。
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