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Detecting Deception in Natural Environments Using Incremental Transfer Learning

Muneeb Ahmad Orcid Logo, Abdullah Alzahrani

26th International Conference on Multimodal Interaction

Swansea University Authors: Muneeb Ahmad Orcid Logo, Abdullah Alzahrani

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DOI (Published version): 10.1145/3678957.3685702

Abstract

Existing work on detecting deception has mainly relied on collecting datasets evolving from contrived user interactions. We argue that naturally occurring deception behaviours can inform more reliable datasets and improve detection rates. Therefore, in this paper, we discuss the findings of two expe...

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Published in: 26th International Conference on Multimodal Interaction
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URI: https://cronfa.swan.ac.uk/Record/cronfa67198
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spelling v2 67198 2024-07-29 Detecting Deception in Natural Environments Using Incremental Transfer Learning 9c42fd947397b1ad2bfa9107457974d5 0000-0001-8111-9967 Muneeb Ahmad Muneeb Ahmad true false d2f9f67e9bfd515f861a917fe1d00321 Abdullah Alzahrani Abdullah Alzahrani true false 2024-07-29 MACS Existing work on detecting deception has mainly relied on collecting datasets evolving from contrived user interactions. We argue that naturally occurring deception behaviours can inform more reliable datasets and improve detection rates. Therefore, in this paper, we discuss the findings of two experiments which enabled participants to freely and naturally engage in deceptive and truthful behaviours in a game environment. We collected physiological and oculomotor behaviour (PB, \& OB) data including electrodermal activity, blood volume pulse, heart rate, skin temperature, blinking rate, and blinking duration during the deceptive and truthful states. We investigate the changes in both PB and OB across repeated interactions and explore the potential of incremental transfer learning in detecting deception. We found significant differences in electrodermal activity, and skin temperature between deception and non-deception groups in both studies. The incremental transfer learning method with a logistic regression classifier detected deception with 80\% accuracy, outperforming previous research. These results highlight the importance of collecting data from multiple sources and promote the use of incremental transfer learning to accurately detect deception in real time. Conference Paper/Proceeding/Abstract 26th International Conference on Multimodal Interaction 0 0 0 0001-01-01 10.1145/3678957.3685702 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2024-09-16T12:05:18.1825881 2024-07-29T09:32:31.6096749 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Muneeb Ahmad 0000-0001-8111-9967 1 Abdullah Alzahrani 2 67198__31067__aaca1e59df094d8ba7c4601aa376eed1.pdf ICMI_2024___Deception_Paper.pdf 2024-08-08T12:40:36.8195148 Output 638143 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Detecting Deception in Natural Environments Using Incremental Transfer Learning
spellingShingle Detecting Deception in Natural Environments Using Incremental Transfer Learning
Muneeb Ahmad
Abdullah Alzahrani
title_short Detecting Deception in Natural Environments Using Incremental Transfer Learning
title_full Detecting Deception in Natural Environments Using Incremental Transfer Learning
title_fullStr Detecting Deception in Natural Environments Using Incremental Transfer Learning
title_full_unstemmed Detecting Deception in Natural Environments Using Incremental Transfer Learning
title_sort Detecting Deception in Natural Environments Using Incremental Transfer Learning
author_id_str_mv 9c42fd947397b1ad2bfa9107457974d5
d2f9f67e9bfd515f861a917fe1d00321
author_id_fullname_str_mv 9c42fd947397b1ad2bfa9107457974d5_***_Muneeb Ahmad
d2f9f67e9bfd515f861a917fe1d00321_***_Abdullah Alzahrani
author Muneeb Ahmad
Abdullah Alzahrani
author2 Muneeb Ahmad
Abdullah Alzahrani
format Conference Paper/Proceeding/Abstract
container_title 26th International Conference on Multimodal Interaction
institution Swansea University
doi_str_mv 10.1145/3678957.3685702
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description Existing work on detecting deception has mainly relied on collecting datasets evolving from contrived user interactions. We argue that naturally occurring deception behaviours can inform more reliable datasets and improve detection rates. Therefore, in this paper, we discuss the findings of two experiments which enabled participants to freely and naturally engage in deceptive and truthful behaviours in a game environment. We collected physiological and oculomotor behaviour (PB, \& OB) data including electrodermal activity, blood volume pulse, heart rate, skin temperature, blinking rate, and blinking duration during the deceptive and truthful states. We investigate the changes in both PB and OB across repeated interactions and explore the potential of incremental transfer learning in detecting deception. We found significant differences in electrodermal activity, and skin temperature between deception and non-deception groups in both studies. The incremental transfer learning method with a logistic regression classifier detected deception with 80\% accuracy, outperforming previous research. These results highlight the importance of collecting data from multiple sources and promote the use of incremental transfer learning to accurately detect deception in real time.
published_date 0001-01-01T12:05:17Z
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score 11.028798

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