Privacy-Preserving Face Redaction Using Crowdsourcing

Abstract

Redaction of private information from images is the kind of tedious, yet context-independent, task for which crowdsourcing is especially well suited. Despite tremendous progress, machine learning is not keeping pace with the needs of sensitive applications in which inadvertent disclosure could have real-world consequences. Human workers can detect faces that machines cannot; however, an open call to crowds would entail disclosure. We present IntoFocus, a method for engaging crowd workers to redact faces from images without disclosing the facial identities of people depicted. The method works iteratively, starting with a heavily filtered form of the image, and gradually reducing the strength of the filter, with a different set of workers reviewing the image at each step. IntoFocus exploits the gap between the filter level at which a face becomes unidentifiable and the level at which it becomes undetectable. To calibrate the algorithm, we performed a perceptual study of detection and identification of faces in images filtered with the median filter. We present the system design, the results of the perception study, and the results of a summative evaluation of the system