Learning privacy-preserving optics via adversarial training

A non-transitory computer-readable storage medium comprising a computer-readable program for acquiring privacy-enhancing encodings in an optical domain before image capture, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:

• feeding a differentiable sensing model with a plurality of images to obtain encoded images, the differentiable sensing model including parameters for sensor optics;
• integrating the differentiable sensing model into an adversarial learning framework where parameters of attack networks, parameters of utility networks, and the parameters of the sensor optics are concurrently updated;
• and
• once adversarial training is complete, validating efficacy of a learned sensor design by fixing the parameters of the sensor optics and training the attack networks and the utility networks to learn to estimate private and public attributes, respectively, from a set of the encoded images.

A system for acquiring privacy-enhancing encodings in an optical domain before image capture, the system comprising:

• a differentiable sensing model fed with a plurality of images to obtain encoded images, the differentiable sensing model including parameters for sensor optics;
• and
• an adversarial learning framework integrated with the differentiable sensing model where parameters of attack networks, parameters of utility networks, and the parameters of the sensor optics are concurrently updated;
• wherein, once adversarial training is complete, validating efficacy of a learned sensor design by fixing the parameters of the sensor optics and training the attack networks and the utility networks to learn to estimate private and public attributes, respectively, from a set of the encoded images.