  Online signature verification technologies, such as those available in banks
and post offices, rely on dedicated digital devices such as tablets or smart
pens to capture, analyze and verify signatures. In this paper, we suggest a
novel method for online signature verification that relies on the increasingly
available hand-worn devices, such as smartwatches or fitness trackers, instead
of dedicated ad-hoc devices. Our method uses a set of known genuine and forged
signatures, recorded using the motion sensors of a hand-worn device, to train a
machine learning classifier. Then, given the recording of an unknown signature
and a claimed identity, the classifier can determine whether the signature is
genuine or forged. In order to validate our method, it was applied on 1980
recordings of genuine and forged signatures that we collected from 66 subjects
in our institution. Using our method, we were able to successfully distinguish
between genuine and forged signatures with a high degree of accuracy (0.98 AUC
and 0.05 EER).
