AI & Machine Learning in Deception Detection
Research Landscape
All 100 Studies
Deception Detection: Using Machine Learning to Analyze 911 Calls
Patrick M. Markey, Jennie Dapice,… · Personality and Social Psychology Bulletin
The random forest model achieved 68.2% overall accuracy with 68.7% sensitivity and 67.7% specificity. Performance varied by context: homicide reports…
A multilayer perceptron neural network prediction approach to polygraph scoring
Dana Rad, Csaba Kiss, Gavril… · INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
The MLP neural network achieved 80% correct classification rate predicting deception levels in 400 offender examinations based on four psychophysiological…
A Multimodal Polygraph Framework with Optimized Machine Learning for Robust Deception Detection
Omar Osama Mohamed Shalash; Ahmed… · MDPI Journals
The study addresses critical gaps by providing an open multimodal dataset and demonstrating improved generalization through PSO-enhanced machine learning approaches…
Artificial intelligence in lie detection: Why do cognitive theories matter?
Philip Tseng, Tony Cheng · New Ideas in Psychology
Mechanism-driven theories enable researchers to identify what features AI extracts and how those features relate to deception, allowing forensic experts…
Artificial Intelligence in Witness Credibility Assessment: The Role of Biometrics, Voice Analytics and Machine Learning in Judicial Cross-Examination
Muhammad Raees Malik, Hafiz Omer… · Journal of Asian Development Studies
Neural Networks achieved 97% accuracy and 98% recall in witness credibility assessment, with 98% true positive rate, significantly outperforming voice…
Cognitive Computing Frameworks for Scalable Deception Detection in Textual Data
Faiza Belbachir · Big Data and Cognitive Computing
CoTAM with sentence decomposition achieved statistically significant improvements (p < 0.05) over baseline methods. LLaMA-3-8B reached F1_lie of 0.35 compared…
DecepTech AI: Advancing Lie Detection Through Multimodal Nonverbal Behavior Analysis
Priyansh Bindroo, Rahi Bhuva, Shruti… · Lecture Notes in Networks and Systems
The system successfully integrated multiple nonverbal behavioral channels using machine learning to detect deception patterns, demonstrating capability for real-time processing…
Development of a Deep-Learning-Based Computerized Scoring Algorithm for Polygraph Data
Korean Research Team · MDPI Journals
Developed a Korean polygraph scoring system using deep neural networks. DNN-based algorithm outperformed both PolyScore and OSS-3 on test data…
Effective faking of verbal deception detection with target-aligned adversarial attacks
Bruno J. Verschuere · Legal and Criminological Psychology
Target-aligned adversarial attacks using GPT-4 reduced both human and machine deception detection performance to chance levels (d = -0.07 to…
Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection
Steven Triplett, Simon Minami, Rakesh… · Lecture Notes in Computer Science
The study found improvements in accuracy over baseline models, with named entity information augmentation producing notable improvements of up to…
Explainable AI for Unraveling the Significance of Visual Cues in High Stakes Deception Detection
Abir Jaafar Hussain · IEEE Access
Eyebrow movements emerged as the most reliable visual indicator of deception in high-stakes courtroom settings. The Multi-layer Perceptron model achieved…
Exploring Audio-Visual Fusion Methods in Foundation Model-Based Deception Detection
Jiaxiang Meng, Hardik B. Sailor,… · 2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Audio-visual fusion using Q-Former architecture with foundation models consistently outperformed unimodal approaches, with systems aggregating embeddings from all layers achieving…
Multi-source Multimodal Progressive Domain Adaption for Audio-Visual Deception Detection
Ronghao Lin, Sijie Mai, Ying… · Proceedings of the 1st International Workshop & Challenge on Subtle Visual Computing
The MMPDA framework achieved 60.43% accuracy and 56.99% F1-score on cross-domain deception detection, surpassing the 1st place team by 5.59%…
Multimodal Deception Detection Using Linguistic and Acoustic Features
Tien Nguyen, Faranak Abri, Akbar… · Machine Learning, Deep Learning and AI for Cybersecurity
A single-layer BiLSTM model achieved 93.57% accuracy and 94.48% F1 score on textual data from court testimony transcripts. ResNet50 performed…
Multimodal Machine Learning for Deception Detection Using Behavioral and Physiological Data
CogniModal-D Research Team · Scientific Reports (2025)
Created multimodal dataset spanning EEG, ECG, EOG, eye-gaze, GSR, audio, and video from 100+ subjects. Demonstrated machine learning integration of…
Recognition of emotions by analysing facial expressions with FaceReader (Noldus) vs detection of deception by polygraph examination: A pilot study
Jan Stefan Widacki; Michał Widacki;… · European Polygraph
The study explored the integration of facial expression analysis technology with traditional polygraph examination to identify emotions accompanying deceptive responses…
Teaching AI Agents to Lie at Social Deduction Games
Matt Eland · Game AI Uncovered
The system successfully enabled AI agents to deceive human players and detect deception by maintaining probabilistic worldviews for each player,…
Tetralet Attention Enabled Modified N-Adam Optimised Distributed Capsule Network for Lie Detection from Electroencephalogram Signals
Anand Ashok Ingle, Jayant P.… · International Journal of Biomedical Engineering and Technology
Specific results unavailable due to inaccessibility of the full paper text.
The Artificial Intelligence Application Concept in the System of Polygraph-Based Psychophysiological Lie Detection
Dmitry V. Belykh-Silaev · Juridical Psychology
The paper establishes a comprehensive conceptual framework defining AI application in polygraph systems, identifying implementation mechanisms, guiding principles, and operational…
Truth Under Pressure: A Deep Learning-Based Lie Detection System for Online Lending Using Voice Stress and Response Latency
Ahmad Ihsan Farhani, Alhadi Bustamam,… · International Journal of Advanced Computer Science and Applications
The lie detection model achieved 70% testing accuracy with 70.9% F1-Score when analyzing voice stress and response latency from court…
You really don't recognise him? The eye-tracker as a forensic tool for concealed knowledge detection
Weronika Celniak; Dominika Słapczyńska; Gowthami… · European Polygraph
The study explored whether eye-tracking combined with deep learning can objectively determine recognition of individuals even when examinees deny familiarity,…
A Review of Automatic Lie Detection from Facial Features
Hugues Delmas, Vincent Denault, Judee… · Journal of Nonverbal Behavior
Automated facial lie detection systems achieve average accuracy of 61.87% to 72.93% depending on classifier type, based on average datasets…
A semi-supervised high-quality pseudo labels algorithm based on multi-constraint optimization for speech deception detection
Huawei Tao, Hang Yu, Man… · Computer Speech & Language
The HQPL-MC algorithm successfully improved deception detection performance through multi-constraint optimization of pseudo-labels, combining noise-augmented feature extraction, autoencoder-based dimensionality reduction,…
Application of Artificial Neural Network in Deception Detection Based on Visual Cues
Hoang Bao Vy Dinh, Quang… · IFMBE Proceedings
The LRCN (Long-term Recurrent Convolutional Network) model achieved the highest classification accuracy of just over 70%, outperforming feature-based LSTM approaches…
Automated Comparative Predictive Analysis of Deception Detection in Convicted Offenders Using Polygraph with Random Forest, Support Vector Machine, and Artificial Neural Network Models
Dana RAD, Csaba KISS, Nicolae… · Studies in Informatics and Control
Machine learning models successfully predicted deception from polygraph physiological data, with clustering analyses revealing important individual differences in response patterns.…
Automatic deceit detection through multimodal analysis of High-Stake Court-Trials
Bicer, B., Dibeklioglu, H. · IEEE Transactions on Affective Computing
The proposed architecture improved state-of-the-art performance on the Real-Life Trial dataset, with all classifiers achieving better results than measured human…
Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks
Panfeng Li, Mohamed Abouelenien, Rada… · 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
The bimodal CNN successfully integrated physiological measurements (heart rate, skin conductance, respiration, skin temperature) with linguistic features from transcripts, demonstrating…
Deception detection using machine learning and deep learning techniques: A systematic review
Shanjita Akter Prome · Natural Language Processing Journal
The review identifies that human deception detection accuracy remains at only 54%, barely better than chance, while modern facial expression-based…
Deception Indicators Prediction for Lie Detection in Dialogues
Chengwei Ji · 2024 16th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)
Full paper not accessible for analysis.
Detecting Deception in Natural Environments Using Incremental Transfer Learning
Muneeb Imtiaz Ahmad, Abdullah Alzahrani,… · International Conference on Multimodel Interaction
Incremental transfer learning with logistic regression achieved 80% deception detection accuracy. Electrodermal activity and skin temperature showed consistent significant differences…
Detecting deception with artificial intelligence: promises and perils
Kristina Suchotzki; Matthias Gamer · Trends in Cognitive Sciences
AI-based deception detection systems lack sufficient scientific validation and may repeat the mistakes of polygraph technology, potentially leading to systematic…
Domain-Agnostic Adapter Architecture for Deception Detection: Extensive Evaluations with the DIFrauD Benchmark
Dainis A. Boumber, Fatima Zahra… · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
The domain-agnostic adapter architecture achieved state-of-the-art performance on the DIFrauD benchmark across seven fraud domains, consistently outperforming all competing methods…
Experimental economics for machine learning—a methodological contribution on lie detection
Dmitri Bershadskyy, Laslo Dinges, Marc-André… · PLOS ONE
Machine learning algorithms trained on video data from an economic dice-rolling experiment achieved 67% lie detection accuracy when ground truth…
Flexible-Modal Deception Detection with Audio-Visual Adapter
Zhaoxu Li, Zitong Yu, Xun… · 2024 IEEE International Joint Conference on Biometrics (IJCB)
The AVA framework achieved superior performance compared to other multi-modal fusion methods under flexible-modal settings, with audio modality generally outperforming…
From Polygraphs to Truth Machines: Artificial Intelligence in Lie Detection
Jo Ann Oravec · Critical Humanities
AI-enhanced lie detection systems introduce novel concerns around 'biomarkers of deceit,' algorithmic opacity, invasive data collection, and bias, potentially enabling…
LIE DETECTION USING FACIAL MICRO-EXPRESSION FEATURES WITH MACHINE LEARNING
· MINAR International Journal of Applied Sciences and Technology
The Sobel filter outperformed Canny edge detection, and mouth/nose regions proved more discriminative than eyes for lie detection, achieving F1-scores…
Physiological Reactions Profiling in Polygraph Testing: Insights from Fuzzy C-Means Clustering Analysis
Dana Rad, Nicolae Paraschiv, Csaba… · Acta Polytechnica Hungarica
Fuzzy clustering identified distinct physiological profiles across 18 parameters with a moderate R² of 0.371 and a low silhouette coefficient…
Polygraph-based deception detection and Machine Learning: Combining the Worst of Both Worlds?
Alex Biedermann; Kyriakos N. Kotsoglou · Forensic Science International: Synergy
The study concludes that applying machine learning to inherently problematic polygraph data does not address the fundamental scientific validity issues…
Training on dirty labels: Rejoinder to Kotsoglou and Biedermann
Dmitri Asonov · Forensic Science International: Synergy
The authors defend their ML approach as practically useful for quality assurance despite training on potentially fallible human labels, arguing…
Video-Based Deception Detection and Financial Fraud
Binglei Duan, Danqi Hu, Hai… · SSRN Electronic Journal
Visual deception scores derived from IPO roadshow videos significantly predict financial fraud. The effect is strongest for deception cues related…
A Deep Learning Approach for Multimodal Deception Detection
Krishnamurthy, G., Majumder, N., Poria,… · Lecture Notes in Computer Science
The multimodal deep learning approach achieved 96.14% accuracy and 0.98 ROC-AUC, significantly outperforming prior automated methods (77-92% AUC) and human…
Automated Deception Detection from Videos: Using End-to-End Learning Based High-Level Features and Classification Approaches
Dinges, L., Fiedler, M.A., Al-Hamadi,… · arXiv preprint
Facial expressions consistently outperformed gaze and head pose modalities, with multimodal fusion achieving AUC scores ranging from 0.65 in low-stakes…
Building a second-opinion tool for classical polygraph
Dmitri Asonov; Mikhail Krylov; Vladislav… · Nature Scientific Reports
The study successfully created a system that can catch mistakes in historical polygraph records by identifying examiner errors in interpreting…
Counter-Deception Information Fusion Based on Reinforcement Learning Using the Guidance of Belief Entropy and Conflict Management
Bingyi Kang ·
The reinforcement learning framework successfully detects deceptive evidence that provides little support to correct targets while supporting incorrect ones, outperforming…
Deception Detection Deep Learning Comprehensive system Utilizing Explainable AI
Suhaib Salah, Tarek Khater, Eqab… · 2023 16th International Conference on Developments in eSystems Engineering (DeSE)
The paper presents a conceptual framework integrating five sensing modalities with deep transfer learning and XAI techniques to create transparent,…
Deception Detection in Videos Using Robust Facial Features with Attention Feedback
Anastasis Stathopoulos, Ligong Han, Norah… · Handbook of Dynamic Data Driven Applications Systems
The Bayesian neural network model achieved 92.36% accuracy detecting deception from facial features alone, with an attention mechanism that identifies…
Deception Detection using a Multimodal Stacked Bi-LSTM Model
Puneet Kumar Sehrawat, Rajat Kumar,… · 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)
The multimodal stacked Bi-LSTM model achieved significant improvements in deception detection compared to single-modality models, with multimodal approaches showing 10-15%…
Deception detection with machine learning: A systematic review and statistical analysis
Alex Sebastião Constâncio; Denise Fukumi… · PLoS ONE
Of 81 reviewed studies, detection accuracy ranged from 51% to 100%, with 19 works achieving above 90% accuracy. Monomodal approaches…
Hybrid Machine Learning Model for Lie-Detection
Rupali J Dhabarde, D. V.… · 2023 IEEE 8th International Conference for Convergence in Technology (I2CT)
The hybrid CNN-SVM model achieved approximately 58% accuracy in detecting deception from facial thermal images, demonstrating the technical feasibility of…
Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions
Haya Alaskar · Computers, Materials & Continua
The study demonstrates a novel hybrid approach combining self-adaptive population-based firefly algorithm optimization with deep learning (MobileNet + LSTM) for…
Increased Interpretability for Model-Driven Deception: MARS LDRD Project
William Hofer, Oceane MS Bel,… ·
The project successfully increased interpretability of machine learning models for industrial control system physical processes, allowing cyber analysts and incident…
Intelligent techniques for deception detection: a survey and critical study
Wasiq Khan · Soft Computing
Machine learning and deep learning methods have demonstrated significant effectiveness in deception detection across multiple modalities, though current datasets lack…
LieVis: A Visual Interactive Dashboard for Lie Detection Using Machine Learning and Deep Learning Techniques
Shanjita Akter Prome, Md Rafiqul… · 2023 26th International Conference on Computer and Information Technology (ICCIT)
BERT deep learning model, along with logistic regression and K-Nearest Neighbors, achieved 84% accuracy in detecting deception from courtroom testimony…
Meta Learning Based Deception Detection from Speech
Noa Mansbach, Amos Azaria · Applied Sciences
CHAML with Wav2Vec 2.0 embeddings achieved 61.34% accuracy in person-independent deception detection, outperforming baseline approaches while using only a few…
Neural network applications in polygraph scoring — A scoping review
Daniel Rad; Nicoleta Paraschiv; Csaba… · MDPI Journals
Neural networks, particularly feedforward and convolutional architectures, show promise in improving polygraph accuracy over traditional methods, though further validation research…
Applying Neural Networks in Polygraph Testing
Lev A. Derevyagin; Veniamin V.… · Journal of Computer and Systems Sciences International
The voting ensemble neural network approach demonstrated increased efficiency in polygraph testing and decreased erroneous conclusions by combining multiple classifier…
Attempt at Detection of Deception Based on Records of Physiological Reactions Remotely Captured with FaceReader Software: Part 1
Jan Stefan Widacki; Bartosz Wójcik;… · European Polygraph
The study represents a pioneering attempt to validate automated facial expression analysis for deception detection, though specific diagnostic performance results…
Attention-Aware Multi-modal RNN for Deception Detection
Shun-Wen Hsiao, Cheng-Yuan Sun · 2022 IEEE International Conference on Big Data (Big Data)
The attention-aware multimodal RNN achieved 96% accuracy on the Real-Life Trial dataset, substantially exceeding human performance (50%) and earlier multimodal…
Automatic deception detection system based on hybrid feature extraction techniques
Shaimaa Hameed Abd, Ivan A.… · Indonesian Journal of Electrical Engineering and Computer Science
The hybrid deception detection system achieved 88.06% classification accuracy by combining FACS-based action units, head movement patterns, and eye gaze…
Dataset Bias in Deception Detection
Ara Mambreyan, Elena Punskaya, Hatice… · 2022 26th International Conference on Pattern Recognition (ICPR)
A gender-prediction classifier achieved comparable performance (64.6% and 59.3% AUC) to state-of-the-art deception detection systems on the Real-life Trial dataset,…
Deception Detection And Vulnerability Analysis Using A Multi-Level Clustering Machine Learning Algorithm In Business Transactions
Aditya Kapoor · SSRN Electronic Journal
The multi-level clustering machine learning technique produced the greatest outcomes for fraud detection, while straightforward anomaly detection methods produced the…
In detecting deception using Decision Tree and SVM across different cues
O. Owolafe, A.O. Ayeni · Nigerian Journal of Technology
The fusion of visual and audio features achieved 97.6% accuracy in deception detection, significantly outperforming either verbal-only or nonverbal-only approaches,…
Looking for Lies: An Exploratory Analysis for Automated Detection of Deception
·
Word count-based machine learning models were most accurate at detecting deception (76% accuracy), with similar performance across video and text-based…
Lying trolls: Detecting deception and text-based disinformation using machine learning
Alex V. Mbaziira, Maha F.… · Cybersecurity and Cognitive Science
Hybrid models combining disinformation training data with other cybercrime text datasets achieved detection accuracies of 60-100%, substantially outperforming singleton models…
Machine Learning Based Deception Detection System in Online Social Networks
Harun BİNGOL, Bilal ALATAS · International Journal of Pure and Applied Sciences
Seven supervised ML methods demonstrated varying performance in deception detection across two datasets when evaluated using four different metrics, providing…
Multimodal Deception Detection Using Real-Life Trial Data
Şen, M.U., Perez-Rosas, V., Yanikoglu,… · IEEE Transactions on Affective Computing
The multimodal system achieved 83.05-84.18% accuracy in detecting deception at the subject level, with the best performance obtained by combining…
Progress of Keystroke Dynamics in Deception Research
Tian-Yu Nan · Fa Yi Xue Za Zhi (Journal of Forensic Medicine)
Keystroke dynamics research has achieved 75-95% accuracy in detecting deception across various contexts, with studies using unexpected questions and machine…
Recurrent Neural Networks for Deception Detection in Videos
Bryan Rodriguez-Meza, Renzo Vargas-Lopez-Lavalle, Willy… · Communications in Computer and Information Science
The Stacked GRU model achieved exceptional performance with 93.69% accuracy and an AUC of 0.9853, surpassing all other tested architectures…
The Application of Machine Learning in Deception Detection : a study based on the effect of empathy on deception detection
Buxue Wang, Xunbing Shen · 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)
The research investigates whether empathy levels moderate human performance when using ML-based deception detection systems, potentially informing personalized approaches to…
The politics of deceptive borders: 'biomarkers of deceit' and the case of iBorderCtrl
Sánchez-Monedero, J., Dencik, L. · Information, Communication & Society
The iBorderCtrl system showed severe limitations including training on only 32 actors with demographic bias, detection accuracy of only 75%,…
A Novel Hybrid Network Model Based on Attentional Multi-Feature Fusion for Deception Detection
Yuanbo FANG, Hongliang FU, Huawei… · IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
The attention-based multi-feature fusion approach significantly outperformed traditional fusion methods and achieved advanced performance using only small amounts of labeled…
Affect-Aware Deep Belief Network Representations for Multimodal Unsupervised Deception Detection
Leena Mathur, Maja J Mataric · 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
The unsupervised model trained on facial valence and visual features achieved 80% AUC (70% accuracy, 88% precision), substantially exceeding typical…
Affect-Aware Machine Learning Models for Deception Detection
Leena Mathur · Proceedings of the AAAI Conference on Artificial Intelligence
Multimodal models incorporating facial affect achieved 91% AUC for deception detection, while unsupervised approaches using facial valence reached 80% AUC,…
Automatic Deception Detection using Multiple Speech and Language Communicative Descriptors in Dialogs
Huang-Cheng Chou, Yi-Wen Liu, Chi-Chun… · APSIPA Transactions on Signal and Information Processing
The multi-modal model achieved 80.61% accuracy detecting deception in Mandarin dialogs, with analysis revealing that deceiver behaviors can be observed…
Deception Detection Using Machine Learning
Alberto Alejandro Ceballos Delgado, William… · Proceedings of the Annual Hawaii International Conference on System Sciences
Multiple machine learning algorithms successfully detected deception in reviews, news articles, and emails, with SVM and Random Forest showing strong…
Explaining Neural Network Results by Sensitivity Analysis for Deception Detection
Xuecheng Zhang, Xuanying Zhu · Communications in Computer and Information Science
Neural networks analyzing observer physiological responses achieved 62% deception detection accuracy, exceeding typical human performance of 54%. Sensitivity analysis combined…
How humans impair automated deception detection performance
Kleinberg, B., Verschuere, B. · Acta Psychologica
Machine learning alone achieved 69% accuracy in detecting deception, but human involvement through hybrid decision-making reduced performance to chance level…
Lie to my face: An electromyography approach to the study of deceptive behavior
Anastasia Shuster; Lilah Inzelberg; Ori… · Brain and Behavior
Machine learning analysis of facial muscle activity achieved 73% accuracy in detecting lies, revealing two distinct groups: those who betrayed…
Reconciling Adapted Psychological Profiling with the New European Data Protection Legislation
Keeley Crockett · Studies in Computational Intelligence
The paper identifies a fundamental challenge in reconciling black-box artificial neural network systems with GDPR's requirement that individuals receive meaningful…
Application of Machine Learning in Deception Detection
Owolafe Otasowie · Advances in Intelligent Systems and Computing
The neurofuzzy model achieved 97.1% accuracy with nonverbal cues alone, outperforming verbal-only (84.3%) and combined approaches (92.5%), while decision tree…
Automated deception detection of males and females from non-verbal facial micro-gestures
Wasiq Khan · IEEE World Congress on Computational Intelligence - IJCNN 2020
Both males and females showed reduced deception detection accuracy when classified by mixed-gender trained models rather than gender-specific classifiers, revealing…
Deception in the eyes of deceiver: A computer vision and machine learning based automated deception detection
Wasiq Khan · Expert Systems with Applications
Eye movements emerged as the most significant predictors of deception, with Random Forest classification achieving 78% accuracy using the 24…
Moth Monarch Optimization-Based Deep Belief Network in Deception Detection System
NIDHI SRIVASTAVA, SIPI DUBEY · Sādhanā
The MMO-DBN system achieved exceptionally high classification performance with 98.4% accuracy, 98.36% sensitivity, and 93.75% specificity in deception detection.
Robust Deep Learning Ensemble against Deception
Wenqi Wei, Ling Liu · IEEE Transactions on Dependable and Secure Computing
XEnsemble achieves high defense success rates against eleven adversarial attack methods and effectively detects out-of-distribution inputs, outperforming existing defense methods…
Video Based Deception Detection Using Deep Recurrent Convolutional Neural Network
Sushma Venkatesh, Raghavendra Ramachandra, Patrick… · Communications in Computer and Information Science
The deep recurrent convolutional neural network architecture outperformed four existing state-of-the-art techniques in detecting deception from real-life trial video footage…
Automatic deception detection in RGB videos using facial action units
Avola, D., Cinque, L., Foresti,… · Proceedings of the 13th International Conference on Distributed Smart Cameras (ICDSC)
The automated facial analysis system achieved 76.84% accuracy in deception detection using only facial Action Units, demonstrating the potential of…
Bag-of-Lies: A Multimodal Dataset for Deception Detection
Gupta, V., Agarwal, M., Arora,… · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Multimodal fusion significantly outperformed single-modality approaches, with the best accuracy of 70% achieved by combining video, audio, and EEG data,…
Box of Lies: Multimodal deception detection in dialogues
Soldner, F., Pérez-Rosas, V., Mihalcea,… · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Multimodal machine learning classification achieved 69% accuracy in detecting conversational deception, exceeding both random baseline (50%) and human performance, with…
Detecting Deception Using Neurofuzzy Approach
Otasowie Owolafe · International Journal of Intelligent Computing Research
The neurofuzzy model achieved 97.1% accuracy detecting deception from nonverbal features alone, significantly outperforming both human judges and chance-level detection.…
Improved semi-supervised autoencoder for deception detection
Hongliang Fu, Peizhi Lei, Huawei… · PLOS ONE
The SS-ANE model achieved 61.81% and 63.89% accuracy on the Killer corpus with only 100 and 200 labeled examples respectively,…
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
Lai, V., Tan, C. · Proceedings of the Conference on Fairness, Accountability, and Transparency
Explanations alone produced only modest improvements in human deception detection (from 51.1% baseline), while showing ML predicted labels yielded over…
Real Time Deception Detection for Criminal Investigation
Imasha Lakshan, Lumini Wickramasinghe, Sandaru… · 2019 National Information Technology Conference (NITC)
The PREDICTOR system demonstrated feasibility for real-time monitoring of deception, emotion, and attentiveness during criminal investigations, though specific accuracy metrics…
Speech Deception Detection Algorithm Based on SVM and Acoustic Features
Huawei Tao, Peizhi Lei, Mengzhe… · 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)
The AF-SVM algorithm successfully demonstrated automated deception detection from speech using Support Vector Machine classification of acoustic features, validating the…
Automated Verbal Credibility Assessment of Intentions: The Model Statement Technique and Predictive Modeling
Kleinberg, B., van der Toolen,… · Applied Cognitive Psychology
Machine learning classification achieved 80% accuracy in initial testing but dropped to 63% in independent validation, with liars paradoxically including…
Comparative Analysis of Classification Methods for Automatic Deception Detection in Speech
Alena Velichko, Viktor Budkov, Ildar… · Lecture Notes in Computer Science
Random Forest achieved the highest performance with 79.3% UAR, while k-Nearest Neighbor combined with ensemble methods reached 76.3% UAR, substantially…
Covert lie detection using keyboard dynamics
Monaro, M., Galante, C., Spolaor,… · Scientific Reports
Keystroke analysis distinguished liars from truth-tellers with 95% accuracy, with liars showing significantly longer reaction times, more typing errors, and…
Intelligent Deception Detection through Machine Based Interviewing
Wasiq Khan · IEEE International Joint Conference on Neural Networks (IJCNN)
Neural network classifiers successfully distinguished truthful from deceptive behavior in avatar interview scenarios, demonstrating that machine-based interviewing can elicit analyzable…
Multimodal deception detection
Burzo, M., Abouelenien, M., Perez-Rosas,… · The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition
Multimodal fusion of linguistic, physiological, and thermal features achieved detection accuracies of 60-75%, with thermal forehead features outperforming other facial…
Multivariate data analysis of polygraph charts for assessment of biophysical characteristics
Minakova, N.N., Bozhich, E.V. · Izv. AltGU
Both PCA and PLS methods successfully analyzed polygraph data to assess human biophysical characteristics and identify physiological response anomalies, with…
Using Blink Rate to Detect Deception: A Study to Validate an Automatic Blink Detector and a New Dataset of Videos from Liars and Truth-Tellers
Borza, D., Itu, R., Danescu,… · Journal of Imaging
The automated system achieved 96.15% accuracy in classifying deceptive versus truthful responses based on blink rate patterns, with 99.3% accuracy…