Detecting deception in natural environments using incremental transfer learning

Ahmad, Muneeb Imtiaz, Alzahrani, Abdullah and Ahmad, Sunbul M. 2024. Detecting deception in natural environments using incremental transfer learning. Presented at: ISMI '24: International Conference on Multimodal Interaction, San Jose, Costa Rica, 4-8 November 2024. ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction. International Conference on Multimodel Interaction. New York, NY: Association for Computing Machinery, pp. 66-75. 10.1145/3678957.3685702

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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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery
ISBN:	9798400704628
Date of First Compliant Deposit:	8 November 2024
Last Modified:	08 Nov 2024 14:22
URI:	https://orca.cardiff.ac.uk/id/eprint/173758

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