Automated Deception Detection Research Articles

Automatic Detection of Verbal Deception in Romanian With Artificial Intelligence Methods

Automatic deception detection is an important task with several applications in both direct physical human communication, as well as in computer-mediated one. The objective of this paper is to study the nature of deceptive language. The primary goal of this study is to investigate deception in Romanian written communication. We created a number of artificial intelligence models (based on Support Vector Machine, Random Forest, and Artificial Neural Network) to detect dishonesty in a topic-specific corpus. To assess the efficiency of the Linguistic Inquiry and Word Count (LIWC) categories in Romanian, we conducted a comparison between multiple text representations based on LIWC, TF-IDF, and LSA. The results show that in the case of datasets with a common subject such as the one we used regarding friendship, text categorization is more successful using general text representations such as TF-IDF or LSA. The proposed approach achieves an accuracy of the classification of 91.3%, outperforming the similar approaches presented in the literature. These findings have implications in fields like linguistics and opinion mining, where research on this subject in languages other than English is necessary. Keywords: Deception Detection, Text Classification, Natural Language Processing, Machine Learning.

Exploring facial cues: automated deception detection using artificial intelligence

Deception detection is an interdisciplinary field attracting researchers from psychology, criminology, computer science, and economics. Automated deception detection presents unique challenges compared to traditional polygraph tests, but also offers novel economic applications. In this spirit, we propose an approach combining deep learning with discriminative models for deception detection. Therefore, we train CNNs for the facial modalities of gaze, head pose, and facial expressions, allowing us to compute facial cues. Due to the very limited availability of training data for deception, we utilize early fusion on the CNN outputs to perform deception classification. We evaluate our approach on five datasets, including four well-known publicly available datasets and a new economically motivated rolling dice experiment. Results reveal performance differences among modalities, with facial expressions outperforming gaze and head pose overall. Combining multiple modalities and feature selection consistently enhances detection performance. The observed variations in expressed features across datasets with different contexts affirm the importance of scenario-specific training data for effective deception detection, further indicating the influence of context on deceptive behavior. Cross-dataset experiments reinforce these findings. Notably, low-stake datasets, including the rolling dice Experiment, present more challenges for deception detection compared to the high-stake Real-Life trials dataset. Nevertheless, various evaluation measures show deception detection performance surpassing chance levels. Our proposed approach and comprehensive evaluation highlight the challenges and potential of automating deception detection from facial cues, offering promise for future research.

Borderline decisions?: Lack of justification for automatic deception detection at EU borders

Between 2016 and 2019, the European Union funded the development and testing of a system called “iBorderCtrl”, which aims to help detect illegal migration. Part of iBorderCtrl is an automatic deception detection system (ADDS): Using artificial intelligence, ADDS is designed to calculate the probability of deception by analyzing subtle facial expressions to support the decision-making of border guards. This text explains the operating principle of ADDS and its theoretical foundations. Against this background, possible deficits in the justification of the use of this system are pointed out. Finally, based on empirical findings, potential societal ramifications of an unjustified use of ADDS are discussed.

Deception/Truthful Prediction Based on Facial Feature and Machine Learning Analysis

The Automatic Deception detection refers to the investigative practices used to determine whether person is telling you Truth or lie. Automatic deception detection has been studied extensively as it can be useful in many real-life scenarios in health, justice, and security systems. Many psychological studies have been reported for deception detection. Polygraph testing is a current trending technique to detect deception, but it requires human intervention and training. In recent times, many machine learning based approaches have been applied to detect deceptions. Various modalities like Thermal Imaging, Brain Activity Mapping, Acoustic analysis, eye tracking. Facial Micro expression processing and linguistic analyses are used to detect deception. Machine learning techniques based on facial feature analysis look like a promising path for automatic deception detection. It also works without human intervention. So, it may give better results because it does not affect race or ethnicity. Moreover, one can do covert operation to find deceit using facial video recording. Covert Operation may capture the real personality of deceptive persons. By making combination of various facial features like Facial Emotion, Facial Micro Expressions and Eye blink rate, pupil size, Facial Action Units we can get better accuracy in Deception Detection.

Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review

Interest in detecting deceptive behaviours by various application fields, such as security systems, political debates, advanced intelligent user interfaces, etc., makes automatic deception detection an active research topic. This interest has stimulated the development of many deception-detection methods in the literature in recent years. This work systematically reviews the literature focused on facial cues of deception. The most relevant methods applied in the literature of the last decade have been surveyed and classified according to the main steps of the facial-deception-detection process (video pre-processing, facial feature extraction, and decision making). Moreover, datasets used for the evaluation and future research directions have also been analysed.

Speech Deception Detection Based on EMD and Temporal Neural Network

Deceptive behaviour is a common phenomenon in human society. Research has shown that humans are not good at distinguishing deception, so studying automated deception detection techniques is a critical task. Most of the relevant technologies are susceptible to personal and environmental influences: EEG‐based technologies need large and expensive equipment, facial‐based technologies are sensitive with the camera’s perspective, and these reasons have somewhat limited the development of applications for deception detection technologies. In contrast, the equipment required for speech deception detection is cheap and easy to use, and the capture of speech is highly covert. Based on the application of signal decomposition algorithms in other fields such as EEG signals and speech emotion recognition, this paper proposed a signal decomposition and reconstruction method based on EMD to process the speech signal and a better deception detection performance was obtained by improving the speech quality. The comparison results with other decomposition algorithms showed that the EMD decomposition algorithm is the most suitable for our method. Across many different classification algorithms, accuracy improved by an average of 2.05% and the F1 score improved by an average of 1.7%. In addition, a new deception detector, called the TCN‐LSTM network, was proposed in this paper. Experiments showed that this network organically combines the processing capability of TCN and LSTM for time series data; the recognition rate of deception detection was greatly improved, with the highest accuracy and F1 score reaching 86.2% and 86.0% under the EMD‐based signal decomposition reconstruction method. Based on the research in this paper, the signal decomposition algorithms need to be further optimised for speech signals and more classification algorithms not used for this task should be tried.

Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

Deception Detection Within and Across Domains: Identifying and Understanding the Performance Gap

NLP approaches to automatic deception detection have gained popularity over the past few years, especially with the proliferation of fake reviews and fake news online. However, most previous studies of deception detection have focused on single domains. We currently lack information about how these single-domain models of deception may or may not generalize to new domains. In this work, we conduct empirical studies of cross-domain deception detection in five domains to understand how current models perform when evaluated on new deception domains. Our experimental results reveal a large gap between within and across domain classification performance. Motivated by these findings, we propose methods to understand the differences in performances across domains. We formulate five distance metrics that quantify the distance between pairs of deception domains. We experimentally demonstrate that the distance between a pair of domains negatively correlates with the cross-domain accuracies of the domains. We thoroughly analyze the differences in the domains and the impact of fine-tuning BERT based models by visualization of the sentence embeddings. Finally, we utilize the distance metrics to recommend the optimal source domain for any given target domain. This work highlights the need to develop robust learning algorithms for cross-domain deception detection that generalize and adapt to new domains and contributes toward that goal.

LieNet: A Deep Convolution Neural Network Framework for Detecting Deception

Nowadays, automatic deception detection has received considerable attention in the machine learning community owing to this research interest to its vast applications in the fields of social media, interviews, law enforcement, and the military. In this study, a novel deep convolution neural network (DCNN) named LieNet is proposed to precisely detect the multiscale variations of deception automatically. Our approach is a combination of contact and noncontact-based approaches. First, 20 frames from each video are fetched and concatenated to form a single image. Moreover, an audio signal is extracted from video and treated as image input by plotting the signal into 2-D plane. Furthermore, 13 channels of electroencephalogram signals are plotted into 2-D plane and concatenated to generate an image. Second, the LieNet model extracts features from each modality separately. Third, scores are estimated using a softmax classifier for all the modalities. Finally, three scores are combined using score level fusion to obtain a score, which gives support in favor of either deception or truth. The LieNet is validated on the “Bag-of-Lies (BoL),” “ real-life (RL) trail,” and “Miami University Deception Detection (MU3D)” databases by considering four evaluation indexes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">viz.,</i> accuracy, precision, recall, and F1-score. Experimental outcomes depict that the LieNet defeats an initial work on Set-A and Set-B of the BoL database with average accuracies of 95.91% and 96.04%. respectively. The accuracies obtained by the LieNet are 97% and 98% on RL trail and MU3D databases respectively.

Automatic deception detection system based on hybrid feature extraction techniques

Human face &lt;span lang="EN-US"&gt;is considered as a rich source of non-verbal features. These features have proven their efficiency, so they are used by the deception detection system (DDS) to distinguish liar from innocent subjects. The suggested DDS utilized three kinds of features, these are facial expressions, head movements and eye gaze. Facial expressions are simply encoded and represented in the form of action units (AUs) based on facial action coding system (FACS). Head movements are represented based on both transitions and rotation. For eye gaze features, the eye gaze directional angle in both x-axis and y-axis are extracted. The collected database used to prove validity and robustness of the suggested system contains videos for 102 subjects from both genders with age range 18-55 years. The detection accuracy of the suggested DDS based on applying the logistic regression classifier is equal to 88.0631%. The proposed system has proven its robustness and the achievement of the highest detection accuracy when compared with previously designed systems.&lt;/span&gt;

Fine-Grained Question-Level Deception Detection via Graph-Based Learning and Cross-Modal Fusion

Automated deception detection has been found as a vital and concerned task, capable of assisting human users to assess truthfulness and detect deceptive behaviors in several situations (e.g., medical, legal, as well as occupational domains). As contact-free video cameras and data analysis techniques are leaping forward, leveraging one&#x2019;s visual, acoustic and textual information captured in a video can be cost-effective for deception detection. In this study, we aim at a fine-grained question-level deception detection task, focusing on detecting whether a subject lies or not in answering each question rather than providing an overall &#x201C;lie or not&#x201D; judgement. A Graph-based Cross-modal Fusion Model (GCFM) is presented to learn the inherent associations among the subject&#x2019;s reactions to different questions, plus a novel cross-modal attention mechanism to enhance the model&#x2019;s learning capability. As revealed by the experimental results on the two datasets, the proposed graph-based GCFM outperformed eight other methods, and its two alternatives (clustering K-means based and sequential learning LSTM based methods), achieving accuracy 88.14&#x0025; and 86.91&#x0025; on the two datasets, respectively. Besides, through association learning, GCFM could increase the accuracy by 1.87&#x0025; and 4.33&#x0025; on the two datasets, respectively. Furthermore, its cross-modal attention mechanism led to the improvement of accuracy by 2.44&#x0025; and 2.95&#x0025; on the two datasets, respectively.

Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework

The automatic detection of deceptive behaviors has recently attracted the attention of the research community due to the variety of areas where it can play a crucial role, such as security or criminology. This work is focused on the development of an automatic deception detection system based on gaze and speech features. The first contribution of our research on this topic is the use of attention Long Short-Term Memory (LSTM) networks for single-modal systems with frame-level features as input. In the second contribution, we propose a multimodal system that combines the gaze and speech modalities into the LSTM architecture using two different combination strategies: Late Fusion and Attention-Pooling Fusion. The proposed models are evaluated over the Bag-of-Lies dataset, a multimodal database recorded in real conditions. On the one hand, results show that attentional LSTM networks are able to adequately model the gaze and speech feature sequences, outperforming a reference Support Vector Machine (SVM)-based system with compact features. On the other hand, both combination strategies produce better results than the single-modal systems and the multimodal reference system, suggesting that gaze and speech modalities carry complementary information for the task of deception detection that can be effectively exploited by using LSTMs.

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

AbstractAutomatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.

Affect-Aware Machine Learning Models for Deception Detection

Automated deception detection systems can enhance societal well-being by helping humans detect deceivers and support people in high-stakes situations across health, social work, and legal domains. Existing computational approaches for detecting deception have not leveraged dimensional representations of affect, specifically valence and arousal, expressed during communication. My research presents a novel analysis of the potential for including affect in machine learning models for detecting deception. My work informs and motivates the development of affect-aware machine learning approaches for modeling deception and other social behaviors during human interactions in-the-wild. This research, independently defined and conducted by me, is from work-in-progress towards my undergraduate thesis in the Department of Computer Science at the University of Southern California.

Automated Deception Detection Systems, a Review

Humans use deception daily since it can significantly affect their life and provide a getaway solution for any undesired situation. Deception is either related to low-stakes (e.g. innocuous) or high-stakes (e.g. with harmful situations). Deception investigation importance has increased, and it became a critical issue over the years with the increase of security levels around the globe. Technology has made remarkable achievements in many human life fields, including deception detection. Automated deception detection systems (DDSs) are widely used in different fields, especially for security purposes. The DDS is comprised of multiple stages, each of which should be built/trained to perform intelligently so that the whole system can give the right decision of whether the involved person is telling the truth or not. Thus, different artificial intelligent (AI) algorithms have been utilized by the researchers over the past years. In addition, there are different cues for DDS that have been considered for the previous works, which are either related to verbal or non-verbal cues. This paper presents a review on the basic methods and the used deception detection techniques for the recent 10 years, that were studied and performed in the field of DDS, with a comparison of the deception detection accuracy reached and the number of participants used for system training.

How humans impair automated deception detection performance

BackgroundDeception detection is a prevalent problem for security practitioners. With a need for more large-scale approaches, automated methods using machine learning have gained traction. However, detection performance still implies considerable error rates. Findings from different domains suggest that hybrid human-machine integrations could offer a viable path in detection tasks. MethodWe collected a corpus of truthful and deceptive answers about participants' autobiographical intentions (n = 1640) and tested whether a combination of supervised machine learning and human judgment could improve deception detection accuracy. Human judges were presented with the outcome of the automated credibility judgment of truthful or deceptive statements. They could either fully overrule it (hybrid-overrule condition) or adjust it within a given boundary (hybrid-adjust condition). ResultsThe data suggest that in neither of the hybrid conditions did the human judgment add a meaningful contribution. Machine learning in isolation identified truth-tellers and liars with an overall accuracy of 69%. Human involvement through hybrid-overrule decisions brought the accuracy back to chance level. The hybrid-adjust condition did not improve deception detection performance. The decision-making strategies of humans suggest that the truth bias - the tendency to assume the other is telling the truth - could explain the detrimental effect. ConclusionsThe current study does not support the notion that humans can meaningfully add the deception detection performance of a machine learning system. All data are available at https://osf.io/45z7e/.

Deception in the eyes of deceiver: A computer vision and machine learning based automated deception detection

There is growing interest in the use of automated psychological profiling systems, specifically applying machine learning to the field of deception detection. Several psychological studies and machine-based models have been reporting the use of eye interaction, gaze and facial movements as important clues to deception detection. However, the identification of very specific and distinctive features is still required. For the first time, we investigate the fine-grained level eyes and facial micro-movements to identify the distinctive features that provide significant clues for the automated deception detection. A real-time deception detection approach was developed utilizing advanced computer vision and machine learning approaches to model the non-verbal deceptive behavior. Artificial neural networks, random forests and support vector machines were selected as base models for the data on the total of 262,000 discrete measurements with 1,26,291 and 128,735 of deceptive and truthful instances, respectively. The data set used in this study is part of an ongoing programme to collect a larger dataset on the effects of gender and ethnicity on deception detection. Some observations are made based on this data which should not be interpreted as scientific conclusions, but pointers for future work. Analysis of the above models revealed that eye movements carry relatively important clues to distinguish truthful and deceptive behaviours. The research outcomes align with the findings from forensic psychologists who also reported the eye movements as distinctive for the truthful and deceptive behavior. The research outcomes and proposed approach are beneficial for human experts and has many applications within interdisciplinary domains.

LieToMe: An Ensemble Approach for Deception Detection from Facial Cues.

Deception 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. polygraph or magnetic resonance, the subject is aware of being monitored and can change his behavior, thus compromising the interrogation result. For this reason, video analysis-based methods for automatic deception detection are receiving ever increasing interest. In this paper, a deception detection approach based on RGB videos, leveraging both facial features and stacked generalization ensemble, is proposed. First, a face, which is well-known to present several meaningful cues for deception detection, is identified, aligned, and masked to build video signatures. These signatures are constructed starting from five different descriptors, which allow the system to capture both static and dynamic facial characteristics. Then, video signatures are given as input to four base-level algorithms, which are subsequently fused applying the stacked generalization technique, resulting in a more robust meta-level classifier used to predict deception. By exploiting relevant cues via specific features, the proposed system achieves improved performances on a public dataset of famous court trials, with respect to other state-of-the-art methods based on facial features, highlighting the effectiveness of the proposed method.

The Impact of Key Ideas on Automatic Deception Detection in Text

In recent years, with the rise of the Internet, the automatic deception detection in text is an important task to recognize those of documents that try to make people believe in something false. Current studies in this field assume that the entire document containscues to identify deception; however, as demonstrated in this work, some irrelevant ideas in text could affect the performance of the classification. Therefore, this research proposes an approach for deception detectionin text that identifies, in the first instance, key ideas in a document based on a topic modeling algorith mand a proposed automatic extractive text summarization method, to produce a synthesized document that avoids secondary ideas. The experimental results of this study indicate that the proposed method outperform previous methods with standard collections.

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision.

Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection.