Preserving privacy is a growing concern in our society where cameras are ubiquitous. In this work, we propose a trainable image acquisition method that removes the sensitive information in the optical domain before it reaches the image sensor. The method benefits from a trainable optical convolution kernel, which transmits the desired information whilst filtering out the sensitive information, making it irretrievable against different privacy attacks in the digital domain. This is in contrast with the current digital privacy-preserving methods that are all vulnerable to direct access attacks. Also, in contrast with most of the previous optical privacy-preserving methods that cannot be trained, our method is data-driven and optimized for the specific application at hand. Moreover, there is no additional computation or power burden on the acquisition system since it works passively in the optical domain and can be even used in conjunction with other privacy-preserving techniques in the digital domain. We demonstrate our new, generic method in several scenarios such as smile or open-mouth detection as the desired attribute while the gender or wearing make-up is filtered out as the sensitive content. Through several experiments, we show that this method is able to reduce around$\mathbf {65}\%$of sensitive content while causing a negligible reduction in the desired information. Moreover, we tested our method by deep reconstruction attack and confirmed the ineffectiveness of this attack to reconstruct the original sensitive content. This new method has different use cases such as feedback systems for smart TV content or outdoor advertising.

Despite the substantial progress made in deep learning in recent years, advanced approaches remain computationally intensive. The trade-off between accuracy and computation time and energy limits their use in real-time applications on low power and other resource-constrained systems. In this paper, we tackle this fundamental challenge by introducing a hybrid optical-digital implementation of a convolutional neural network (CNN) based on engineering of the point spread function (PSF) of an optical imaging system. This is done by coding an imaging aperture such that its PSF replicates a large convolution kernel of the first layer of a pre-trained CNN. As the convolution takes place in the optical domain, it has zero cost in terms of energy consumption and has zero latency independent of the kernel size. Experimental results on two datasets demonstrate that our approach yields more than two orders of magnitude reduction in the computational cost while achieving near-state-of-the-art accuracy, or equivalently, better accuracy at the same computational cost.