Detection of simple and complex deceits through facial micro-expressions: a comparison between human beings’ performances and machine learning techniques

Micro-expressions have gained increasing interest in the last few years, both in scientific and professional contexts. Theoretically, their emergence suggests ongoing concealments, making them arguably one of the most reliable cues for lie detection (e.g., Yan, Wang, Liu, Wu & Fu, 2014; Venkatesh, Ramachandra & Bours, 2019). Given their fast onset, they result almost imperceptible to the eye of an untrained subject, making it necessary to work on automatic detection tools. Machine learning models have shown promisingly results within this domain; thus, the aim of the study at hand, was to compare the performances human judges and machine learning models obtain on the same dataset of stimuli. Regrettably, machine learning performances have ended up being around the chance level, positing the question of why previous and a-like studies have collected better results. Briefly, insights on how to properly organize an experimental paradigm and collect a dataset for lie detection studies are discussed, while concluding that among other several necessary cues it is still crucial to consider micro-expressions when dealing with lie detection procedures

Contextual considerations for deception production and detection in forensic interviews

7 pagesMost deception scholars agree that deception production and deception detection effects often display mixed results across settings. For example, some liars use more emotion than truth-tellers when discussing fake opinions on abortion, but not when communicating fake distress. Similarly, verbal and nonverbal cues are often inconsistent predictors to assist in deception detection, leading to mixed accuracies and detection rates. Why are lie production and detection effects typically inconsistent? In this piece, we argue that aspects of the context are often unconsidered in how lies are produced and detected. Greater theory-building related to contextual constraints of deception are therefore required. We reintroduce and extend the Contextual Organization of Language and Deception (COLD) model, a framework that outlines how psychological dynamics, pragmatic goals, and genre conventions are aspects of the context that moderate the relationship between deception and communication behavior such as language. We extend this foundation by proposing three additional aspects of the context — individual differences, situational opportunities for deception, and interpersonal characteristics — for the COLD model that can specifically inform and potentially improve forensic interviewing. We conclude with a forward-looking perspective for deception researchers and practitioners related to the need for more theoretical explication of deception and its detection related to the context

“My Mum was a cop…”: A mixed methods exploration of deceptive performance using the General Expertise Framework.

The General Expertise Framework (GEF) explains the phenomenon that regardless of domain, experts have certain features in common, such as a high volume of accumulated practice, performance consistency across time and situation, accuracy of calibration between perceived and actual performance, and well-developed metaawareness which facilitates adaptability of performance in response to feedback. Interpersonal Deception Theory (IDT) and Activation-Decision-Construction- ActionTheory (ADCAT) present lying as a cognitively challenging act requiring skill to perform well. So, it makes sense that deception should show the same features as other areas of expertise. However, this has never been systematically tested. This programme of research involved four empirical studies, across a range of channels and contexts including interactive in-person interviews and online written deception, which sought to answer an overarching question. Can deceptive performance be conceptualised as a skill, as defined by the GEF? To obtain an objective measure of deceptive performance uncontaminated by possible receiver biases, a Matrix of measures was constructed which included only the most reliable cues. The results suggest that deception is a particular example of expertise, learned in a wicked environment, poorly practiced by most and situationally contingent. Expert liars show an effect of practice, but a high volume of accumulated practice is not sufficient to confer expertise, rather focused, strategic use of lying is required. Expert liars demonstrate superior calibration of perceived and actual performance even though feedback on lying is almost nonexistent in everyday life. This may be why responsiveness to feedback is the most challenging element of expertise in the domain of deception. The unique insights provided by the mixed-methods approach means future research must continue to explore these techniques

The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans

A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false information campaigns with targeted manipulation of public opinion on specific topics. These false information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one, and needs increased public awareness, as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community. In this paper, we make a step in this direction by providing a typology of the Web's false information ecosystem, comprising various types of false information, actors, and their motives. We report a comprehensive overview of existing research on the false information ecosystem by identifying several lines of work: 1) how the public perceives false information; 2) understanding the propagation of false information; 3) detecting and containing false information on the Web; and 4) false information on the political stage. In this work, we pay particular attention to political false information as: 1) it can have dire consequences to the community (e.g., when election results are mutated) and 2) previous work show that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false information dissemination on the Web

Securities Fraud, Recidivism, and Deterrence

Legal scholars have expended considerable energy on the study of high-level securities fraud violators-Ken Lay, Bernie Ebbers, Dennis Kozlowski, etc. There has been little attention, however, to the perpetrators of retail securities fraud-the con artists who sell bogus stock over the Internet, orchestrate elaborate pump-and-dump schemes, and create a never-ending array of purportedly risk free investment opportunities. Collectively, and in a cruel mockery of capitalism, these offenders extract hundreds of millions dollars from investors each year. In this article, Professor Barnard examines this group of offenders, focusing particularly on those who recidivate-often moving from state to state and scheme to scheme, with little interruption from the law enforcement community. She hypothesizes that offenders in this group, much like sex offenders, may be hard wired to engage in fraudulent behavior. Even if that is not the case, however, these offenders present a much greater risk to the public than the current SEC enforcement regime contemplates. She proposes a series of new enforcement strategies to deal with this predatory population

Securities Fraud, Recidivism, and Deterrence

Legal scholars have expended considerable energy on the study of high-level securities fraud violators-Ken Lay, Bernie Ebbers, Dennis Kozlowski, etc. There has been little attention, however, to the perpetrators of retail securities fraud-the con artists who sell bogus stock over the Internet, orchestrate elaborate pump-and-dump schemes, and create a never-ending array of purportedly risk free investment opportunities. Collectively, and in a cruel mockery of capitalism, these offenders extract hundreds of millions dollars from investors each year. In this article, Professor Barnard examines this group of offenders, focusing particularly on those who recidivate-often moving from state to state and scheme to scheme, with little interruption from the law enforcement community. She hypothesizes that offenders in this group, much like sex offenders, may be hard wired to engage in fraudulent behavior. Even if that is not the case, however, these offenders present a much greater risk to the public than the current SEC enforcement regime contemplates. She proposes a series of new enforcement strategies to deal with this predatory population

Exploiting Group Structures to Infer Social Interactions From Videos

In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features and a novel class of meta-features we callLiarRank. We develop a LiarOrNot model to identify spies in Resistance videos. We achieve AUCs of over 0.70 outperforming our baselines by 3% and human judges by 12%. For the problem of political deception, we first collect a dataset of videos and transcripts of 76 politicians from 18 countries making truthful and deceptive statements. We call it the Global Political Deception Dataset. We then show how to analyze the statements in a broader context by building a Video-Article-Topic graph. From this graph, we create a novel class of features called Deception Score that captures how controversial each topic is and how it affects the truthfulness of each statement. We show that our approach achieves 0.775 AUC outperforming competing baselines. Finally, we use the Resistance data to solve the problem of dyadic impression prediction. Our proposed Dyadic Impression Prediction System (DIPS) contains four major innovations: a novel class of features called emotion ranks, sign imbalance features derived from signed graphs theory, a novel method to align the facial expressions of subjects, and finally, we propose the concept of a multilayered stochastic network we call Temporal Delayed Network. Our DIPS architecture beats eight baselines from the literature, yielding statistically significant improvements of 19.9-30.8% in AUC