Conditional Imaging for Privacy-Preserving Visual Sensing

Abstract

Conventional visual sensing faces a fundamental dilemma: information that should remain private today may need to be recovered tomorrow. This challenge is becoming increasingly important as cameras become pervasive in our daily surroundings. Existing safeguards either protect visual content only after an image is formed or suppress information during capture in ways that prevent its later recovery. Here, we show through an information-theoretic analysis that privacy and recoverability cannot be achieved simultaneously in static optical systems. Information preserved in a measurement for later reconstruction also remains available to an observer of that measurement, limiting the privacy such systems can provide. To overcome this limitation, we introduce conditional imaging, an imaging approach that changes the optical state for every captured frame. The measurement alone conceals the scene, while knowledge of the corresponding physical system state enables its reconstruction. We analyze how the optical parameters determine the information that can be recovered and use this analysis to guide the design of a camera prototype. We evaluate the system on a captured dataset of human faces using increasingly powerful attempts to recover the scene, from direct visualization and computational inversion to learned reconstruction and identity recognition. Our experiments show that conditional imaging preserves identity in authorized reconstructions while withholding visual information from observers without access to the system state. By making the physical state of the camera part of the information needed for reconstruction, conditional imaging enables visual information to be protected at the point of capture while remaining recoverable for future authorized use.