Automatically Detecting Bystanders in Photos to Reduce Privacy Risks

Photographs taken in public places often contain bystanders~-- people who are not the main subject of a photo. These photos, when shared online, can reach a large number of viewers and potentially undermine the bystanders' privacy. Furthermore, recent developments in computer vision and machine learning can be used by online platforms to identify and track individuals. To combat this problem, researchers have proposed technical solutions that require bystanders to be proactive and use specific devices and/or applications to broadcast their privacy policy and identifying information while being located in an image. We explore the prospect of a different approach~-- identifying bystanders solely based on the visual information present in an image. Through an online user study, we catalog the rationale humans use to classify subjects and bystanders in an image, and systematically validate a set of intuitive concepts (such as intentionally posing for a photo) that can be used to automatically identify bystanders. Using image data, we infer those concepts and then use them to train several classifier models. We extensively evaluate the models and compare them with human raters. On our training data set, which features a 10-fold cross validation, our best model achieves a mean detection accuracy of 93% for images when human raters have 100% agreement on the class label and 80% when the agreement is only 67%. We validate this model on a completely different test data set and achieve similar results, demonstrating that our model generalizes well.