A Deep Learning Approach for Multimodal Deception Detection

TL;DR

This paper introduces a multi-modal neural network that combines video, audio, text, and micro-expression features to improve deception detection accuracy in real-life videos, achieving state-of-the-art results.

Contribution

The paper presents a novel multi-modal deep learning model that effectively integrates multiple data modalities for deception detection, outperforming existing methods.

Findings

Achieved 96.14% accuracy in deception detection

Attained ROC-AUC of 0.9799 on real-life videos

Demonstrated superiority over previous techniques

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.