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Intelligent techniques for deception detection: a survey and critical study

Alaskar, H, Sbaï, Z, Khan, W, Hussain, A and Alrawais, A (2022) Intelligent techniques for deception detection: a survey and critical study. Soft Computing. ISSN 1432-7643

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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.

Item Type: Article
Additional Information: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00500-022-07603-w
Uncontrolled Keywords: 0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Sciences; Artificial Intelligence & Image Processing
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Springer
SWORD Depositor: A Symplectic
Date Deposited: 08 Dec 2022 13:04
Last Modified: 03 Nov 2023 00:50
DOI or ID number: 10.1007/s00500-022-07603-w
URI: https://researchonline.ljmu.ac.uk/id/eprint/18339
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