24,046 research outputs found
Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel
Wrist-wearables such as smartwatches and fitness bands are equipped with a
variety of high-precision sensors that support novel contextual and
activity-based applications. The presence of a diverse set of on-board sensors,
however, also expose an additional attack surface which, if not adequately
protected, could be potentially exploited to leak private user information. In
this paper, we investigate the feasibility of a new attack that takes advantage
of a wrist-wearable's motion sensors to infer input on mechanical devices
typically used to secure physical access, for example, combination locks. We
outline an inference framework that attempts to infer a lock's unlock
combination from the wrist motion captured by a smartwatch's gyroscope sensor,
and uses a probabilistic model to produce a ranked list of likely unlock
combinations. We conduct a thorough empirical evaluation of the proposed
framework by employing unlocking-related motion data collected from human
subject participants in a variety of controlled and realistic settings.
Evaluation results from these experiments demonstrate that motion data from
wrist-wearables can be effectively employed as a side-channel to significantly
reduce the unlock combination search-space of commonly found combination locks,
thus compromising the physical security provided by these locks
Non-Parametric Probabilistic Image Segmentation
We propose a simple probabilistic generative model for
image segmentation. Like other probabilistic algorithms
(such as EM on a Mixture of Gaussians) the proposed model
is principled, provides both hard and probabilistic cluster
assignments, as well as the ability to naturally incorporate
prior knowledge. While previous probabilistic approaches
are restricted to parametric models of clusters (e.g., Gaussians)
we eliminate this limitation. The suggested approach
does not make heavy assumptions on the shape of the clusters
and can thus handle complex structures. Our experiments
show that the suggested approach outperforms previous
work on a variety of image segmentation tasks
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