Publication Date: 10/24/2022
Event: ECCV 2022
Reference: LNCS 13667, pp. 504–521, 2022
Authors: Zaid Tasneem, Rice University, NEC Laboratories America, Inc.; Giovanni Milione, NEC Laboratories America, Inc.; Yi-Hsuan Tsai, NEC Laboratories America, Inc.; Xiang Yu, NEC Laboratories America, Inc.; Ashok Veeraraghavan, Rice University; Manmohan Chandraker, NEC Laboratories America, Inc., UC San Diego; Francesco Pittaluga, NEC Laboratories America, Inc.
Abstract: With over a billion sold each year, cameras are not only becoming ubiquitous, but are driving progress in a wide range of domains such as mixed reality, robotics, and more. However, severe concerns regarding the privacy implications of camera-based solutions currently limit the range of environments where cameras can be deployed. The key question we address is: Can cameras be enhanced with a scalable solution to preserve users’ privacy without degrading their machine intelligence capabilities? Our solution is a novel end-to-end adversarial learning pipeline in which a phase mask placed at the aperture plane of a camera is jointly optimized with respect to privacy and utility objectives. We conduct an extensive design space analysis to determine operating points with desirable privacy-utility tradeoffs that are also amenable to sensor fabrication and real-world constraints. We demonstrate the first working prototype that enables passive depth estimation while inhibiting face identification.
Publication Link: https://link.springer.com/chapter/10.1007/978-3-031-20071-7_30