Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras

The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances in computer vision techniques have greatly facilitated people's lives in many aspects, but have also inevitably raised people's concerns about visual privacy at the same time. Motivated by recent user studies that people's privacy concerns are dependent on the context, in this paper, we propose Cardea, a context-aware and interactive visual privacy protection framework that enforces privacy protection according to people's privacy preferences. The framework provides people with fine-grained visual privacy protection using: i) personal privacy profiles, with which people can define their context-dependent privacy preferences; and ii) visual indicators: face features, for devices to automatically locate individuals who request privacy protection; and iii) hand gestures, for people to flexibly interact with cameras to temporarily change their privacy preferences. We design and implement the framework consisting of the client app on Android devices and the cloud server. Our evaluation results confirm this framework is practical and effective with 86% overall accuracy, showing promising future for context-aware visual privacy protection from pervasive cameras.