Learning Privacy Preserving Encodings through Adversarial Training

We present a framework to learn privacy-preserving encodings of images that inhibit inference of chosen private attributes, while allowing recovery of other desirable information. Rather than simply inhibiting a given fixed pre-trained estimator, our goal is that an estimator be unable to learn to accurately predict the private attributes even with knowledge of the encoding function. We use a natural adversarial optimization-based formulation for this---training the encoding function against a classifier for the private attribute, with both modeled as deep neural networks. The key contribution of our work is a stable and convergent optimization approach that is successful at learning an encoder with our desired properties---maintaining utility while inhibiting inference of private attributes, not just within the adversarial optimization, but also by classifiers that are trained after the encoder is fixed. We adopt a rigorous experimental protocol for verification wherein classifiers are trained exhaustively till saturation on the fixed encoders. We evaluate our approach on tasks of real-world complexity---learning high-dimensional encodings that inhibit detection of different scene categories---and find that it yields encoders that are resilient at maintaining privacy.Comment: To appear in WACV 201

A low-power structured light sensor for outdoor scene reconstruction and dominant material identification

We introduce a compact structured light device that utilizes a commercially available MEMS mirror-enabled hand-held laser projector. Without complex re-engineering, we show how to exploit the projector's high-speed MEMS mirror motion and laser light-sources to suppress ambient illumination, enabling low-cost and low-power reconstruction of outdoor scenes in sunlight. We discuss how the line-striping acts as a kind of “light-probe”, creating distinctive patterns of light scattered by different types of materials. We investigate visual features that can be computed from these patterns and can reliably identify the dominant material characteristic of a scene, i.e. where most of the objects consist of either diffuse (wood), translucent (wax), reflective (metal) or transparent (glass) materials.</p