Light emanations from flat-panel displays are a side channel hinting towards the displayed content. Optical eavesdropping requires sensors in the proximity of such displays, necessitating physical access to the the target's environment. This requirement may be eliminated by exploiting the light sensor on the target's mobile device, though there are significant challenges. Such sensors measure one-dimensional light intensity, provide no chromatic information, and have very low sampling rate (normally up to 10Hz). In this paper, we demonstrate that in spite of these challenges, it is possible-based on intensity measurements from a mobile device's light sensor-to make quality inferences regarding the displayed content. We do so by selecting features of measured light that capture information related to transitions between samples. Such features are resilient to ambient noise. In our experiments, involving over 60 hours of collected data and 140 movie clips, we were able to (i) classify content into categories (game, movie, etc) with approximately 90% and 70% accuracy for two-class and four-class classification, respectively; and (ii) identify specific movies or TV programs being played with > 85% accuracy. These findings suggest that access to raw light-sensor readings, which can currently be done without special access controls, may carry nontrivial security ramifications.