The shift to video conferencing post-COVID-19 has raised concerns about privacy due to video leaks of participants' on-screen information. Eyeglasses and reflective objects can unintentionally reveal contents to webcams. This research employs mathematical modeling and human subject experiments to assess how emerging webcam technology may leak recognizable textual and graphical information as captured by these reflections. Ultimately, the study aims to anticipate and evaluate the factors influencing the recognizability of content during virtual communications in a constantly evolving technological landscape.
Personal video conferencing has become a new norm after COVID-19. Video leaks participants' on-screen information because eyeglasses and reflective objects expose screen contents.
Using mathematical modeling and human subjects experiments, this research explores the extent to which emerging webcams might leak recognizable textual and graphical information from eyeglass reflections.
The primary goal of this work is to measure, compute, and predict the factors, limits, and thresholds of recognizability as webcam technology evolves.
This work explores and characterizes viable threat models based on optical attacks using reflective surfaces like glasses during video conferencing.
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