The detection of faked identity using unexpected questions and mouse dynamics

PLOS ONE, Dec 2019

The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent’s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to “build” and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee.

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The detection of faked identity using unexpected questions and mouse dynamics

May The detection of faked identity using unexpected questions and mouse dynamics Merylin Monaro 0 1 Luciano Gamberini 1 Giuseppe Sartori 1 0 PhD Program in Brain , Mind and Computer Science , University of Padova , Padova , Italy , 2 University of Padova, Human Inspired Technology Research Centre , Padova , Italy , 3 University of Padova, Department of General Psychology , Padova , Italy 1 Editor: Zhong-Ke Gao, Tianjin University , CHINA The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent's true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to ªbuildº and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee. - Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Introduction The use of faked identities is a very common issue. People can fake their personal information for a number of reasons. Faked autobiographical information is, for example, observed in sports, with players claiming to be younger than what they really are [ 1 ]. Social networks are plagued by faked profiles [ 2 ]. Faked personal identity is also a major issue in security [ 3 ]. In fact, a large number of terrorists are believed to be hidden among migrants from the Middle East entering Europe. Usually, migrants lack documents and their identity information is often based on self-declaration. Among migrants, it is believed that a high number of terrorists are giving false identities when entering borders. For example, one of the terrorists involved in the Brussels airport suicide bombing on March 22, 2016 was using the identity of a former Inter Milan football player [ 4 ]. In these cases, biometric identification tools (e.g., fingerprints) could not be applied as most of the suspects were previously unknown. Interestingly, detection techniques could be, in principle, applied. From the beginning, starting with the pioneer work of Benussi [ 5 ], the identification of deceptive responses has mainly been based on the use of physiological measures [ 6 ]. More recently, reaction time (RT)-based techniques have been introduced. These are based on the response latencies to the presented stimulus of interest. There is wide consensus regarding the fact that deception is cognitively more complex than truth-telling and that this higher cognitive complexity is reflected in a number of indices of cognitive effort, including, for example, reaction times [ 7 ]. There is evidence that the process of inhibiting the truthful response, which is automatically activated, and substituting it with a deceptive response may be a complex cognitive task. However, in some instances, responding with a lie may be faster than truthfully responding [ 8 ]. In fact, distinct types of lies may differ in their cognitive complexity and may require different levels of cognitive effort. For example, the cognitive effort may be minimal when the subject is simply denying a fact that actually happened. By contrast, it could be very high when fabricating complex lies, such as when Ulysses, the hero of The Odyssey, told Polypheme that his real name was ªNo-man.º This lie was intended to fool Polypheme but was also supposed to be easily spotted as a lie by Polypheme's one-eyed companions. RT-based memory detection has a number of advantages over alternative psychophysiological techniques, especially when a high number of subjects are under scrutiny. First, RTs are less sensitive to strong individual or environmental changes, such as in the case of physiological parameters. Secondly, this technique has the unparalleled feature that it may be applied using merely a computer and administered to a large number of examinees over the Web. Currently, two memory-detectio (...truncated)


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Merylin Monaro, Luciano Gamberini, Giuseppe Sartori. The detection of faked identity using unexpected questions and mouse dynamics, PLOS ONE, 2017, Volume 12, Issue 5, DOI: 10.1371/journal.pone.0177851