Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification

Abstract

Identity safekeeping on chats has recently become an important problem on social networks. One of the most important issues is identity theft, where impostors steal the identity of a person, substituting her in the chats, in order to have access to private information. In the literature, the problem has been addressed by designing sets of features which capture the way a person interacts through the chats. However, such approaches perform well only on the long term, after a long conversation has been performed, this is a problem, since in the early turns of a conversation, much important information can be stolen. This paper focuses on this issue, presenting a learning approach which boosts the performance of user recognition and verification, allowing to recognize a subject with considerable accuracy. The proposed method is based on a recent framework of one-shot multi-class multi-view learning, based on Reproducing Kernel Hilbert Spaces (RKHS) theory. Our technique reaches a recognition rate of 76.9% in terms of AUC of the Cumulative Matching Characteristic curve, with only 10 conversational turns considered, on a total of 78 subjects. This sets the new best performances on a public conversation benchmark.