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Deception detection is gaining increasing interest due to ethical andsecurity concerns. This paper explores the application of convolutional neuralnetworks for the purpose of multimodal deception detection. We use a datasetbuilt by interviewing 104 subjects about two topics, with one truthful and onefalsified response from each subject about each topic. In particular, we makethree main contributions. First, we extract linguistic and physiologicalfeatures from this data to train and construct the neural network models.Second, we propose a fused convolutional neural network model using bothmodalities in order to achieve an improved overall performance. Third, wecompare our new approach with earlier methods designed for multimodal deceptiondetection. We find that our system outperforms regular classification methods;our results indicate the feasibility of using neural networks for deceptiondetection even in the presence of limited amounts of data.

Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks