Adversarial Training is a technique used to improve the robustness and resilience of a model by training it against adversarial examples. Adversarial examples are specially crafted inputs that are designed to deceive or compromise the performance of the model. Adversarial training is commonly applied in the context of deep neural networks and is used to enhance the model’s ability to handle unexpected or malicious inputs.

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Learning Phase Mask for Privacy-Preserving Passive Depth Estimation

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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.

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