El-Sappagh S, Ali F, Abuhmed T, Singh J, Alonso JM. Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing [Internet]. 2022;512:203–24. Available from: https://doi.org/10.1016/j.neucom.2022.09.009 I met with Helen and over the following few months she introduced me to seven other women who had similarly been deceived into long term sexual relationships with undercover police officers. It was now clear that the rogue officer story was far from unique and indeed appeared to reflect a pattern. Nevertheless, some individuals show a remarkable ability for spotting deceptions, with a detection accuracy above 90%. Referred to as “Wizards of deception detection” [ 5], these individuals demonstrate that lies can be detected. Such “wizards”, however, are not numerous. Vrij A, Akehurst L, Soukara S, Bull R. Detecting deceit via analyses of verbal and nonverbal behavior in children and adults. Hum Commun Res. 2004;30(1):8–41. Small details about their lives didn't quite fit. Then they vanished, leaving a note saying that the relationship was over. These men were undercover police officers, who had targeted the women for their links to activist groups.

Facial cues are important because the face is the primary vehicle to express someone’s emotional state. Also, they have been the focus of many studies, for current technology provides many tools for extracting facial features. Statistical details can be found in section 4.2 (Visual features analysis) of S6 File (Statistical Analysis Notebook). Ali F, El-Sappagh S, Islam SMR, Ali A, Attique M, Imran M, et al. An intelligent healthcare monitoring framework using wearable sensors and social networking data. Futur Gener Comput Syst [Internet]. 2020;114:23–43. Available from: https://doi.org/10.1016/j.future.2020.07.047 Alzubi JA, Alzubi OA, Beseiso M, Budati AK, Shankar K. Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis. Expert Syst [Internet]. 2022 May 11;39(4). Available from: https://onlinelibrary.wiley.com/doi/10.1111/exsy.12879Alternatively, unsupervised or self-supervised learning happens when the model training does not require labeled data [ 20]. From the qualitative perspective, we interpreted the statistical findings according to some theoretical frameworks on deception detection [ 2, 4, 5]. We discuss how the authors’ approaches align to those frameworks, where they agree and don’t, and what is still to be done. All those comments can be found in the “Discussion” section.

Ekman reports that faking an emotion may be easier (especially for professional actors), but not demonstrating strong ones is almost impossible since some facial expressions involuntarily arise [ 2]. It is said that these emotions “leak out”, betraying the deceiver. Facial expressions, micro-expressions, micro-gestures, and affect were analyzed as features in 32 (39.5%) out of the 81 selected papers, with a myriad of performance levels. The highest one reports 0.97 as accuracy [ 41, 44]. In general, Bimodal and Multimodal approaches show better results than Monomodal ones, a synergy between visual and non-visual cues. These findings demonstrate the importance of visual cues for deception detection. A public inquiry into undercover policing has been running since 2015, but progress has been severely hampered by police requests for secrecy. The hearings restarted this week, however the sessions relating to the women’s experiences are not expected to begin until next year. All those Jupyter Lab Notebooks can be found at GitHub. Here we present only the discussion of our findings, as we consider that our greatest contribution.However, Deep Learning methods rely on large amounts of data to produce results free or with low bias. They also rely heavily on GPU power to be trained, and their architectures can become highly complex. This can be a problem for deception detection since labeled data in unconstrained circumstances is scarce. Then, some authors opted to exploit Autoencoders [ 80, 111], Deep Learning models that do not consume labeled data. Those are very recent works that may show a promising answer to the data scarcity problem. a) Authors modeled deception detection as a classification problem (supervised learning), except for one case that proposed a clustering-based solution (unsupervised learning);

As our contribution to the field, we present a discussion that unfolds in several themes (or dimensions) we consider suitable. Those themes were not chosen. Rather, they arise from the selected documents and represent a general summary of all the efforts analyzed. Those themes are findings themselves. They outline the main topics present in the selected studies regarding the theoretical foundations of deception detection. Authors attempted these approaches to answer to the deception detection problem.We’d thought the men we’d fallen in love with were good people, who shared our passion for making the world a better place when they’d joined our political groups. We had many happy years. But then they started to behave strangely and ultimately vanished, leaving a note explaining that they’d gone abroad to “sort themselves out”. Through forensic detective work, we eventually confirmed we had all been in serious relationships with men who were married police officers and whose deceit was funded by the taxpayer.