Seeing double

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, weaknessnamely 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.

Understanding Shoulder Surfing in the Wild

“…In another study [21], 85 % of the participants acknowledged that they had already observed sensitive information on computer screens they were not authorised to see. In addition to direct observation, threats to visual privacy have recently been discussed in the context of surveillance cameras [38], corneal reflections [53], drones [48] and life logging cameras [22].…”

A TEMPEST Attack Implementation based on Hidden Markov model in Smart Grid

“…By using supervised learning methods, each key is identified by comparing unknown waveforms with known waveforms collected during training. By video recording [2][3][4][5][6], such as researchers reading computer screens through tiny reflections on glasses, teapots, and other objects. Radio signals captured through surrounding wireless infrastructure, such as using dense cellular deployments to detect nearby human activities, passively listening to CRS broadcasts from base stations to identify victim keystrokes, and analyzing the reflected cellular signals, the inputted PIN was restored on ATM.…”