@inproceedings{b271aa404ffe483a9a202e6bc8e54d1f,
title = "Deception Detection Deep Learning Comprehensive system Utilizing Explainable AI",
abstract = "Deception detection plays a vital role in various domains, from security and law enforcement to human behavior analysis. In this paper, we propose a comprehensive system for deception detection that leverages S\&A smart sensing device, deep transfer learning, deep learning techniques, and explainable artificial intelligence. Our approach combines visual, auditory, thermal, cardiovascular, and respiratory cues, offering enhanced accuracy and resistance to countermeasures. Deep Transfer Learning is employed to adapt pre-trained models to the deception detection task, overcoming data limitations. Incorporating Explainable AI techniques enhances transparency and interpretability, fostering trust and collaboration in human-machine interactions. Our research lays the groundwork for future advancements in deception detection technology, addressing challenges and providing promising opportunities in the realm of deception detection.",
keywords = "Artificial, DTL, Deception, Deep, Detection, Explainable, Intelligence, Learning, Multi-Modal, XAI, transfer",
author = "Suhaib Salah and Tarek Khater and Eqab Almajali and Wasiq Khan and Abir Hussain",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 16th International Conference on Developments in eSystems Engineering, DeSE 2023 ; Conference date: 18-12-2023 Through 20-12-2023",
year = "2023",
doi = "10.1109/DeSE60595.2023.10468817",
language = "English",
series = "Proceedings - International Conference on Developments in eSystems Engineering, DeSE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "713--720",
editor = "Obe, \{Dhiya Al-Jumeily\} and Sulaf Assi and Manoj Jayabalan and Jade Hind and Abir Hussain and Hissam Tawfik and Neil Rowe and Jamila Mustafina",
booktitle = "DeSE 2023 - Proceedings",
address = "United States",
}