Learning Phase Mask for Privacy-Preserving Passive Depth Estimation
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others. 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 in this paper 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 of sensor configurations with respect to phase mask design, focus settings and resolution 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.
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