Deception detection using machine learning (ML) and deep learning (DL) techniques: A systematic review

Abstract

Deception detection is a crucial concern in our daily lives, with its effect on social interactions. The human face is a rich source of data that offers trustworthy markers of deception. The deception detection systems are non-intrusive, cost-effective, and mobile by identifying face expressions. Over the last decade, numerous studies have been conducted on deception/lie detection using several advanced techniques. Researchers have given their attention to inventing more effective and efficient solutions for deception detection. However, there are still a lot of opportunities for innovative deception detection methods. Thus, in this literature review, we conduct the statistical analysis by following the PRISMA protocol and extract various articles from five e-databases. The main objectives of this paper are (i) to explain the overview of machine learning (ML) and deep learning (DL) techniques for deception detection, (ii) to outline the existing literature, and (iii) to address the current challenges and its research prospects for further study. While significant issues in deception detection methods are acknowledged, the review highlights key conclusions and offers a systematic analysis of state-of-the-art techniques, emphasizing contributions and opportunities. The findings illuminate current trends and future research prospects, fostering ongoing development in the field.