Intelligent techniques for deception detection: a survey and critical study-Reference-Cited by-同舟云学术

Intelligent techniques for deception detection: a survey and critical study

Published:2022-11-03 Issue:7 Volume:27 Page:3581-3600
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Alaskar Haya^ORCID,Sbaï Zohra,Khan Wasiq,Hussain Abir,Alrawais Arwa

Funder

Prince Sattam bin Abdulaziz University

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-022-07603-w.pdf

Reference102 articles.

1. Abouelenien M, Pérez-Rosas V, Mihalcea R, Burzo M (2016) Detecting deceptive behavior via integration of discriminative features from multiple modalities. IEEE Trans Inf Forens Secur 12:1042–1055

2. Adelson R (2004) Psychological sleuths–Detecting deception [WWW Document]. https://www.apa.org. https://www.apa.org/monitor/julaug04/detecting. Accessed 1 Feb 2021

3. Alaskar H (2018) Deep learning-based model architecture for time-frequency images analysis. Int J Adv Comput Sci Appl 9

4. Al-tahri M, Al-tamimi N, Al-harbi S, Abduallah A, Alaskar H, Sbai Z (2022) Deceptive detection based on spectrum analysis using deep learning. In: 2022 international conference on electrical, computer, communications and mechatronics engineering (ICECCME)

5. Amber F, Yousaf A, Imran M, Khurshid K (2019) P300 based deception detection using convolutional neural network. In: 2019 2nd international conference on communication, computing and digital systems (C-CODE). IEEE, pp 201–204

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