TapPrints: Your Finger Taps Have Fingerprints

摘要

This paper shows that the location of screen taps on modern smart-phones and tablets can he identified from aecelerom-eter and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smart-phones and tablets, and silently monitor the user's inputs, such as keyboard presses arid icon taps. While precise tap detection is non-trivial, requiring machine learning algorithms to identify fingerprints of closely spaced keys, sensitive sensors on modern devices aid the process. We present TapPrints, a framework for inferring the location of taps on mobile device touch-screens using motion sensor data combined with machine learning analysis. By running tests on two different, off-the-shelf smart-phones and a tablet computer we show that-identifying tap locations on the screen and inferring English letters could be done with up to 90% and 80% accuracy, respectively. By optimizing the core tap detection capability with additional information, such as contextual priors, we are able to further magnify the core threat.