Toward Robotic Robbery on the Touch Screen

Despite the tremendous amount of research fronting the use of touch gestures as a mechanism of continuous authentication on smart phones, very little research has been conducted to evaluate how these systems could behave if attacked by sophisticated adversaries. In this article, we present two Lego-driven robotic attacks on touch-based authentication: a population statistics--driven attack and a user-tailored attack. The population statistics-driven attack is based on patterns gleaned from a large population of users, whereas the user-tailored attack is launched based on samples stolen from the victim. Both attacks are launched by a Lego robot that is trained on how to swipe on the touch screen. Using seven verification algorithms and a large dataset of users, we show that the attacks cause the system’s mean false acceptance rate (FAR) to increase by up to fivefold relative to the mean FAR seen under the standard zero-effort impostor attack. The article demonstrates the threat that ro