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Detecting Deception in Natural Environments Using Incremental Transfer Learning

Muneeb Ahmad Orcid Logo, Abdullah Alzahrani, Sunbul M. Ahmad Orcid Logo

ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction, Pages: 66 - 75

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: ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction
ISBN: 979-8-4007-0462-8 979-8-4007-0462-8
Published: New York, NY, USA ACM 2024
URI: https://cronfa.swan.ac.uk/Record/cronfa67198
first_indexed 2024-09-16T11:02:09Z
last_indexed 2025-02-03T20:21:17Z
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spelling 2025-02-03T14:23:31.8735946 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 ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction 66 75 ACM New York, NY, USA 979-8-4007-0462-8 979-8-4007-0462-8 Deception, Measurement, Dataset, Physiological and oculomotor behaviours, Bluff Game, Human-Robot Game Interaction 4 11 2024 2024-11-04 10.1145/3678957.3685702 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2025-02-03T14:23:31.8735946 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 Sunbul M. Ahmad 0009-0009-3685-2452 3 67198__32952__1c4d024209dd4b0d9c701f2cf0f55e34.pdf 67198.VoR.pdf 2024-11-22T14:06:38.9635863 Output 810242 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution International 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
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
Sunbul M. Ahmad
format Conference Paper/Proceeding/Abstract
container_title ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction
container_start_page 66
publishDate 2024
institution Swansea University
isbn 979-8-4007-0462-8
979-8-4007-0462-8
doi_str_mv 10.1145/3678957.3685702
publisher ACM
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
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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 2024-11-04T08:14:41Z
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score 11.055436

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