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      The detection of faked identity using unexpected questions and mouse dynamics

      1 , 2 , 3 , 3 , *

      PLoS ONE

      Public Library of Science

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          Abstract

          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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          Most cited references 35

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 May 2017
                2017
                : 12
                : 5
                Affiliations
                [1 ]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
                Tianjin University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: GS MM.

                • Data curation: MM.

                • Formal analysis: GS MM.

                • Investigation: MM.

                • Methodology: GS MM LG.

                • Supervision: GS.

                • Validation: GS MM LG.

                • Writing – original draft: MM GS.

                • Writing – review & editing: GS MM LG.

                Article
                PONE-D-17-01238
                10.1371/journal.pone.0177851
                5436828
                28542248
                © 2017 Monaro et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 6, Tables: 9, Pages: 19
                Product
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Behavior
                Deception
                People and Places
                Population Groupings
                Ethnicities
                Italian People
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Support Vector Machines
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Memory
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Memory
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Velocity
                Physical Sciences
                Physics
                Classical Mechanics
                Acceleration
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized

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