Multimodal machine learning for deception detection using behavioral and physiological data

• Published in: Scientific reports Vol. 15; no. 1; pp. 8943 - 14
• Main Authors: Joshi, Gargi, Tasgaonkar, Vaibhav, Deshpande, Aditya, Desai, Aditya, Shah, Bhavya, Kushawaha, Akshay, Sukumar, Aadith, Kotecha, Kermi, Kunder, Saumit, Waykole, Yoginii, Maheshwari, Harsh, Das, Abhijit, Gupta, Shubhashi, Subudhi, Akanksha, Jain, Priyanka, Jain, N. K., Walambe, Rahee, Kotecha, Ketan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 692/700; Adult; Affective computing; Automated deception detection; Cognitive behaviour analysis; Deception; EEG; EKG; Electrocardiography; Electroencephalography; Electrooculography; Female; Galvanic skin response; Galvanic Skin Response - physiology; Humanities and Social Sciences; Humans; India; Learning algorithms; Lie detection; Lie Detection - psychology; Machine Learning; Male; multidisciplinary; Multimodal data fusion; Neurophysiological data; Physiology; Science; Science (multidisciplinary); India; Affective computing; Cognitive behaviour analysis; Multimodal data fusion; Automated deception detection; Neurophysiological data; Behavioural data; Lie detection

Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom trials. Differentiating truth from lies is inherently challenging due to many complex, diversified behavioural, physiological and cognitive aspects. Traditional lie detector tests (polygraphs) have be...