Periscope: A Keystroke Inference Attack Using Human Coupled Electromagnetic Emanations

Abstract

This study presents Periscope, a novel side-channel attack that exploits human-coupled electromagnetic (EM) emanations from touchscreens to infer sensitive inputs on a mobile device. Periscope is motivated by the observation that finger movement over the touchscreen leads to time-varying coupling between these two. Consequently, it impacts the screen's EM emanations that can be picked up by a remote sensory device. We intend to map between EM measurements and finger movements to recover the inputs. As the significant technical contribution of this work, we build an analytic model that outputs finger movement trajectories based on given EM readings. Our approach does not need a large amount of labeled dataset for offline model training, but instead a couple of samples to parameterize the user-specific analytic model. We implement Periscope with simple electronic components and conduct a suite of experiments to validate this attack's impact. Experimental results show that Periscope achieves a recovery rate over 6-digit PINs of 56.2% from a distance of 90 cm. Periscope is robust against environment dynamics and can well adapt to different device models and setting contexts.