Abstract

We develop Light Field Messaging (LFM), a process of  embedding, transmitting, and receiving  hidden information in video that is displayed on a screen and captured by a handheld camera. The goal of the system is to minimize  perceived visual artifacts of the message embedding, while simultaneously maximizing the accuracy of message recovery on the camera side. LFM requires photographic steganography for embedding messages that can be displayed and camera-captured. Unlike digital steganography, the embedding requirements are significantly more challenging due to the combined effect of the screen's radiometric emittance function, the camera's sensitivity function, and  the camera-display relative geometry. We devise and train a  network to jointly learn a deep embedding and recovery algorithm that requires no multi-frame synchronization. A key novel component is the  camera display transfer function (CDTF) to model the camera-display pipeline. To learn this CDTF we introduce a dataset (Camera-Display 1M) of 1,000,000 camera-captured images collected from 25 camera-display pairs. The result of this work is a high-performance real-time LFM system using consumer-grade displays and smartphone cameras.