A Deep Learning Approach for Multimodal Deception Detection

Gangeshwar Krishnamurthy; Navonil Majumder; Soujanya Poria; Erik Cambria

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arXiv · 1803.00344

A Deep Learning Approach for Multimodal Deception Detection

Gangeshwar Krishnamurthy·Navonil Majumder·Soujanya Poria·Erik Cambria

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Abstract

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799.

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Gangeshwar Krishnamurthy, Navonil Majumder, Soujanya Poria, Erik Cambria. 2018-03-01. A Deep Learning Approach for Multimodal Deception Detection. https://arxiv.org/abs/1803.00344

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