Conference Paper/Proceeding/Abstract 189 views
Detecting Deception in Natural Environments Using Incremental Transfer Learning
,
Abdullah Alzahrani,
Sunbul M. Ahmad
International Conference on Multimodel Interaction, Pages: 66 - 75
Swansea University Authors:
Muneeb Ahmad , 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...
| Published in: | International Conference on Multimodel Interaction |
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| ISBN: | 979-8-4007-0462-8 979-8-4007-0462-8 |
| Published: |
New York, NY, USA
ACM
2024
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa67198 |
| 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 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. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
66 |
| End Page: |
75 |