Abstract
Of late, threats enabled by the ubiquitous use of mobile
devices have drawn much interest from the research community.
However, prior threats all suffer from a similar, and profound,
weakness --- namely the requirement that the adversary is either
within visual range of the victim (e.g., to ensure that the pop-out
events in reflections in the victim's sunglasses can be discerned) or
is close enough to the target to avoid the use of expensive
telescopes. In this paper, we broaden the scope of the attacks by
relaxing these requirements and show that breaches of privacy are
possible even when the adversary is around a corner. The approach we
take overcomes challenges posed by low image resolution by extending
computer vision methods to operate on small, high-noise,
images. Moreover, our work is applicable to all types of keyboards
because of a novel application of fingertip motion analysis for
key-press detection. In doing so, we are also able to exploit
reflections in the eyeball of the user or even repeated reflections
(i.e., a reflection of a reflection of the mobile device in the
eyeball of the user). Our empirical results show that we can perform
these attacks with high accuracy, and can do so in scenarios that
aptly demonstrate the realism of this threat.
Yi Xu, Jared Heinly, 
