Automated Bystander Detection and Anonymization in Mobile Photography

As smartphones have become more popular in recent years, integrated cameras have seen a rise in use. This trend has negative implications for the privacy of the individual in public places. Those who are captured inadvertently in others’ pictures often have no knowledge of being included in a photograph nor have any control over how the photos of them might be distributed. To address this growing issue, we propose a novel system for protecting the privacy of bystanders captured in public photos. A fully automated approach to accurately distinguish the intended subjects of photos from strangers is first explored. To accurately distinguish these subjects and bystanders, we develop a feature-based classification approach utilizing entire photos. Additionally, we consider the privacy-minded case of only utilizing local face images with no contextual information from the original image by developing a convolutional neural network-based classifier. Considering the face to be the most sensitive and identifiable portion of a bystander, both classifiers are utilized to form an estimation of facial feature locations which can then be obfuscated to protect bystander privacy. We implement and compare three methods of facial anonymization: black boxing, Gaussian blurring, and pose-tolerant face swapping. To validate and explore the viability of these anonymization methods, a comprehensive user survey is conducted to understand the difference in appeal and viability between them.