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The psychophysiological detection of deception
Current Directions in Psychological Science, 1994The psychophysiological detec tion of deception (PDD; also known as polygraphy or lie detection) has been, and remains, an important ap plication of psychology in the real world. These psychophysiological tests involve the recording of auto nomie nervous system indices (e.g., respiratory, electrodermal, cardio vascular, and vasomotor activity) while ...
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MultiModal Deception Detection: Accuracy, Applicability and Generalizability*
International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, 2020The increasing use of Artificial Intelligence (AI) systems in face recognition and video processing in recent times creates higher stakes for their application in daily life.
Vibha Belavadi +7 more
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Hemispheric asymmetry and deception detection
Laterality: Asymmetries of Body, Brain and Cognition, 2005Previous research has indicated a possible right hemisphere advantage in deception detection including a possible left ear advantage in decoding deceptive statements. In this study, 32 undergraduate students listened to 112 true and false statements presented unilaterally to both the left and right ears.
Sarah Malcolm, Julian Paul Keenan
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Multilingual Deception Detection by Autonomous Agents
The Web Conference, 2020In this work we present the development of a multilingual deception detection model based on speech. In addition, we also develop a model that detects whether a statement will be perceived as a lie or not by human subjects.
E. Neiterman, M. Bitan, A. Azaria
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Deception detection and emotion recognition: Investigating F.A.C.E. software
Psychotherapy Research, 2020Objective: Investigation of deception within psychotherapy has recently gained attention. Micro expression training software has been suggested to improve deception detection and enhance emotion recognition.
D. Curtis
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Deception detection expertise.
Law and Human Behavior, 2008A lively debate between Bond and Uysal (2007, Law and Human Behavior, 31, 109-115) and O'Sullivan (2007, Law and Human Behavior, 31, 117-123) concerns whether there are experts in deception detection. Two experiments sought to (a) identify expert(s) in detection and assess them twice with four tests, and (b) study their detection behavior using eye ...
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LieToMe: An Ensemble Approach for Deception Detection from Facial Cues
International Journal of Neural Systems, 2020Deception detection is a relevant ability in high stakes situations such as police interrogatories or court trials, where the outcome is highly influenced by the interviewed person behavior. With the use of specific devices, e.g.
D. Avola +4 more
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2018
Much research has examined people’s ability to correctly distinguish between honest and deceptive communication. The ability to detect deception is useful, but many misconceptions about effective lie detection have been documented. Research on deception is especially informative because the findings of research often contradict common sense.
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Much research has examined people’s ability to correctly distinguish between honest and deceptive communication. The ability to detect deception is useful, but many misconceptions about effective lie detection have been documented. Research on deception is especially informative because the findings of research often contradict common sense.
openaire +2 more sources
Detecting deception in testimony
2008 IEEE International Conference on Intelligence and Security Informatics, 2008Several models for deception in text, based on changes in usage frequency of certain classes of words, have been proposed. These are empirically derived from settings in which individuals are asked to lie or be truthful in freeform text. We consider the problem of detecting deception in testimony, where the content generated must necessarily be ...
David B. Skillicorn, A. Little
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A Deep Learning Approach for Multimodal Deception Detection
Conference on Intelligent Text Processing and Computational Linguistics, 2018Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection.
Gangeshwar Krishnamurthy +3 more
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