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Intelligent techniques for deception detection: a survey and critical study

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Abstract

Machine intelligence methods originated as effective tools for generating learning representations of features directly from the data and have indicated usefulness in the area of deception detection. The success of machine intelligence-based methods covers resolving multiple complex tasks that combine multiple low-level image features with high-level contexts, from feature extraction to classification. The goal of this paper, given this period of rapid evolution, is to provide a detailed overview of the recent developments in the domain of automated deception detection mainly brought about by machine intelligence-based techniques. This study examines about 100 research papers that explores diverse areas of common deception detection through text, speech, and video data analysis. We performed a critical analysis of the existing techniques, tools and available datasets which have been used within the existing works, followed by possible directions for the future developments in this domain.

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Funding

This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project No 2020/01/11744.

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  1. Computer Science Department, College of Engineering and Computer Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia

    Haya Alaskar, Zohra Sbaï & Arwa Alrawais

  2. National Engineering School of Tunis, Tunis El Manar University, Tunis, 1068, Tunisia

    Zohra Sbaï

  3. Computer Science Department, Liverpool John Moores University, Liverpool, L33AF, UK

    Wasiq Khan & Abir Hussain

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Alaskar, H., Sbaï, Z., Khan, W. et al. Intelligent techniques for deception detection: a survey and critical study. Soft Comput 27, 3581–3600 (2023). https://doi.org/10.1007/s00500-022-07603-w

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