Deceptive Behavior Research Articles

Advancements and Applications of Anti-Fraud Graphical Passwords

Visual cryptography represents a sophisticated and innovative approach to secure information sharing, gaining traction within the field of information security. This technology adeptly transforms sensitive data into visually interpretable graphics or images. These images are then segmented into multiple independent parts, each distributed among different stakeholders. This strategy ensures that no single party can access the complete data without collaboration, thereby enhancing security. This dissertation delves into several anti-fraud visual password schemes designed to mitigate risks associated with deceptive behaviors by participants and distributors alike. By addressing these vulnerabilities, the proposed schemes enhance the integrity and reliability of the data sharing process. The application of visual passwords in real-life scenarios not only bolsters data security but also shields private information from potential leaks. By reducing the complexity and cost associated with traditional decryption methods, visual cryptography offers substantial support for a wide array of application scenarios. This dissertation presents a comprehensive exploration of visual cryptography's potential to revolutionize data protection strategies, illustrating its importance and utility in contemporary digital environments.

Minimum adjustment cost consensus model with bounded risk preference attitudes: A perspective based on fractional stochastic dominance

Linguistic multi-attribute group decision making with linguistic distribution assessment (LMAGDM-LDA) problems have been widely investigated. However, in the existing studies, the risk attitudes of decision maker are seldom considered. Inspired by this, in this paper, we employ the idea of fractional stochastic dominance to develop a consensus reaching framework for LMAGDM-LDA with bounded risk preference attitudes. Then, we design an interactive learning algorithm for obtaining the bounded risk preference threshold. Next, we analyze the deception behaviors and average behaviors of experts, and develop the minimum adjustment cost consensus model with bounded risk preference attitude (MACC-BRP). And we justify the connection between the MACC-BRP and the minimum adjustment cost consensus model with other risk attitudes. Furthermore, we provide a distance-based method to obtain the rankings of alternatives. Furthermore, a numerical analysis is provided to show the computation process of the proposed method, and a comparison analysis and a simulation analysis are further conducted to show the advantages and features of the proposal. The simulation results show the effects of the risk preference threshold, the intrinsic individual weight and the average threshold. The comparison results show that the proposed minimum adjustment cost consensus models have clear advantages.

Cortical oscillations and event-related brain potentials during the preparation and execution of deceptive behavior.

Deception often occurs in response to a preceding cue (e.g., a precarious question) alerting us about the need to subsequently lie. Here, we simulate this process by adapting a previously established paradigm of intentionally false responding, now instructing participants about the need for deception (vs. truthful responses) by means of a simple cue occurring before each response-relevant target. We analyzed event-related brain potentials (ERPs) as well as cortical oscillations recorded from the scalp. In an experimental study (N = 44), we show that a cue signaling the need for deception involves increased attentional selection (P2, P3a, P3b). Moreover, in the period following the cue and leading up to the target, ERP and oscillatory signatures of anticipation and preparation (Contingent Negative Variation, alpha suppression) were found to be increased during trials requiring a deceptive as compared to a truthful response. Additionally, we replicated earlier findings that target processing involves enhanced motivated attention toward words requiring a deceptive response (LPC). Moreover, a signature of integration effort and semantic inhibition (N400) was observed to be larger for words to which responses have to be intentionally false as compared to those to which responses must be truthful. Our findings support the view of the involvement of a series of basic cognitive processes (especially attention and cognitive control) when responses are deliberately wrong instead of right. Moreover, preceding cues signaling the subsequent need for lying already elicit attentional and preparatory mechanisms facilitating the cognitive operations necessary for later successful lying.

Motor-evoked potentials as biomarkers for sexual arousal?

Motor cortex excitability may represent the neuronal endpoint of motivational processes and was shown to be modulated by both sexual arousal and deceptive behavior. This is the first study to investigate the influence of lying and sex in heterosexual women and men based on motor-evoked potentials (MEPs) measured while viewing sexually arousing pictures. Sixteen heterosexual couples were shown 360 trials consisting of pictures displaying both almost naked females and males and neutral control images. In a subsequent forced-choice question about wanting to see the respective pictures fully naked, they were instructed to either answer in agreement with or opposite to their sexual preference. Participants went through 2 blocks of answering truthfully and 2 blocks of lying, with these 4 blocks being shown in a randomized alternating order. To measure cortical excitability, MEPs were used, evoked by single transcranial magnetic stimulation pulses between image presentation and response. In normalized MEPs, women and men showed higher amplitudes for preferred over non-preferred sexual stimuli, but only on a descriptive level. Planned contrasts showed higher non-normalized MEPs for lying in all picture categories. Direct comparisons to a preliminary study showed overall lower effect sizes. Both sexes tend to show higher MEPs in response to their sexually preferred stimuli. MEPs are not stable markers for willful volitionally controlled deception although lying does increase cortical excitability. The present experimental design does not seem valid enough to serve as a diagnostic marker for sexual preference or paraphilia and malingering. This is the first study investigating whether sexual motivational stimuli modulate MEPs in women, while also examining the influence of lying for both sexes. The sample was too small for some found effects to be significant. Also, the experimental setup may have been less suited for female participants in comparison to male ones. The operationalization of sexual motivation via MEPs seems to highly depend on different experimental factors including the sex of the participants, induced motivation, and lying.

EEG-Based Lie Detection Using Autoencoder Deep Learning with Muse II Brain Sensing

Detecting deception has significant implications in fields like law enforcement and security. This research aims to develop an effective lie detection system using Electroencephalography (EEG), which measures the brain's electrical activity to capture neural patterns associated with deceptive behavior. Using the Muse II headband, we obtained EEG data across 5 channels from 34 participants aged 16-25, comprising 32 males and 2 females, with backgrounds as high school students, undergraduates, and employees. EEG data collection took place in a suitable environment, characterized by a comfortable and interference-free setting optimized for interviews. The research contribution is the creation of a lie detection dataset and the development of an autoencoder model for feature extraction and a deep neural network for classification. Data preparation involved several pre-processing steps: converting microvolts to volts, filtering with a band-pass filter (3-30Hz), STFT transformation with a 256 data window and 128 overlap, data normalization using z-score, and generating spectrograms from power density spectra below 60Hz. Feature extraction was performed using an autoencoder, followed by classification with a deep neural network. Methods included testing three autoencoder models with varying latent space sizes and two types of classifiers: three new deep neural network models, including LSTM, and six models using pre-trained ResNet50 and EfficientNetV2-S, some with attention layers. Data was split into 75% for training, 10% for validation, and 15% for testing. Results showed that the best model, using autoencoder with latent space size of 64x10x51 and classifier using the pre-trained EfficientNetV2-S, achieved 97% accuracy on the training set, 72% on the validation set, and 71% on the testing set. Testing data resulted in an F1-score of 0.73, accuracy of 0.71, precision of 0.68, and recall of 0.78. The novelty of this research includes the use of a cost-effective EEG reader with minimal electrodes, exploration of single and 3-dimensional autoencoders, and both non-pretrained classifiers (LSTM, 2D convolution, and fully connected layers) and pretrained models incorporating attention layers.

Preschoolers' deception related to prefrontal cortex activation: An fNIRS study

Deception is an essential part of children's moral development. Previous developmental studies have shown that children start to deceive at the age of 3 years, and as age increased to 5 years, almost all children were able to deceive for their own benefit. Although behavioral studies have indicated that the emergence and development of deception are related to cognitive abilities, their neural correlates remain poorly understood. Therefore, the present study examined the neural correlates underlying deception in preschool-aged children (N = 89, 44 % boys, age 3.13 to 5.96 years, Han Chinese) using functional near-infrared spectroscopy. A modified hide-and-seek paradigm was applied to elicit deceptive and truth-telling behaviors. The results showed that activation of bilateral dorsolateral prefrontal cortex was positively associated with the tendency to deceive an opponent in a competitive game in the 3-year-olds. In addition, 3-year-olds who showed a high tendency to deceive showed the same brain activation in the frontopolar area as 5-year-olds did when engaged in deception, whereas no such effect was found in 3-year-olds who never engaged in deception. These findings underscore the link between preschoolers’ deception and prefrontal cortex function.

The Influence of Brand Greenwashing on EV Purchase Intention: The Moderating Role of Consumer Innovativeness and Peer Brand Attitude

In the context of new energy Electric Vehicles (EVs), certain car manufacturers engage in deceptive behaviors known as “greenwashing”, including activities such as “subsidy cheating”, “exaggerating carbon reduction claims”, and “selective disclosure of environmental information”. These behaviors have a negative impact on industry progress. While previous studies suggest that consumers’ perceptions of greenwashing towards individual brands extend to the industry as a whole and influence their overall purchase intentions, there remains a gap in understanding how these behaviors specifically affect consumers’ willingness to purchase EVs. To address this gap and enrich the literature on the relationship between greenwashing and consumer choice, this study uses ABC attitude theory and experimental methods to investigate the impact of greenwashing in the EV sector on consumers’ vehicle preferences in three experiments. The results show that consumers’ perceptions of greenwashing in one EV brand negatively influence their purchase intentions towards other brands, mediated by a general skepticism towards environmental claims in the industry. In addition, consumers’ innovativeness and attitudes towards other brands play a negative moderating role in this relationship. The research findings provide comprehensive insights into the complex impact of brand greenwashing on consumer behavior within the EV industry.

Uncovering corporate greenwashing: a predictive model based on Chinese heavy-pollution industries

Purpose As the global emphasis on environmental consciousness intensifies, many corporations claim to be environmentally responsible. However, some merely partake in “greenwashing” – a facade of eco-responsibility. Such deceptive behavior is especially prevalent in Chinese heavy-pollution industries. To counter these deceptive practices, this study aims to use machine learning (ML) techniques to develop predictive models against corporate greenwashing, thus facilitating the sustainable development of corporations. Design/methodology/approach This study develops effective predictive models for greenwashing by integrating multifaceted data sets, which include corporate external, organizational and managerial characteristics, and using a range of ML algorithms, namely, linear regression, random forest, K-nearest neighbors, support vector machines and artificial neural network. Findings The proposed predictive models register an improvement of over 20% in prediction accuracy compared to the benchmark value, furnishing stakeholders with a robust tool to challenge corporate greenwashing behaviors. Further analysis of feature importance, industry-specific predictions and real-world validation enhances the model’s interpretability and its practical applications across different domains. Practical implications This research introduces an innovative ML-based model designed to predict greenwashing activities within Chinese heavy-pollution sectors. It holds potential for application in other emerging economies, serving as a practical tool for both academics and practitioners. Social implications The findings offer insights for crafting informed, data-driven policies to curb greenwashing and promote corporate responsibility, transparency and sustainable development. Originality/value While prior research mainly concentrated on the factors influencing greenwashing behavior, this study takes a proactive approach. It aims to forecast the extent of corporate greenwashing by using a range of multi-dimensional variables, thus providing enhanced value to stakeholders. To the best of the authors’ knowledge, this is the first study introducing ML-based models designed to predict a company’s level of greenwashing.

A model of academic dishonesty intentions: mediating effects of machiavellianism and differential association

ABSTRACT Past studies of academic dishonesty typically have been grounded in the theory of planned behaviour. Key predictors of academic dishonesty variables under this framework generally have been attitudes towards academic dishonesty, the impact of peer pressure to engage in such acts, and individual perceptions of related acts. However, planned behaviour theory is silent about the significant impact on academic dishonesty of past academic dishonesty behaviours. The objective of the present study is to analyse, through the lens of social learning theory, the effects of past behaviours that predispose academic dishonesty: differential association, i.e., the effects of individual learning, through interaction with others, of the values, attitudes, techniques, and motives for deviant behaviour, on the prediction of academic dishonesty intentions. Additionally, the study tests the effects of Machiavellianism, or dishonest and deceptive behaviours on the relationship between past academic dishonesty acts and academic dishonesty attitudes. Results of the study reveal that differential association with cheating acceptors strengthens the relationship between academic dishonesty attitudes and intentions. Additionally, Machiavellianism at higher levels intensifies the relationships between past academic dishonesty behaviours and contemporary academic dishonesty attitudes.

Catch me if you can: free-living mice show a highly flexible dodging behaviour suggestive of intentional tactical deception.

Intentional tactical deception, the employment of a tactic to intentionally deceive another animal, is a complex behaviour based on higher-order cognition, that has rarely been documented outside of primates and corvids. New laboratory-to-field assays, however, provide the opportunity to investigate such behaviour among free-living mice. In the present study, we placed laboratory-style test chambers with a single entrance near a forest outside Warsaw, where we observed the social interactions of two territorial murids, black-striped and yellow-necked mice, under food competition for seven months. Notably, among the social interactions, we video-recorded 21 instances of deceptive pursuer evasion. In the most obvious cases, an individual inside the chamber, to avoid an incoming mouse, hid by the chamber opening (the only means to enter or exit), paused until the pursuer entered and passed by, and then exploited the distraction of the back-turned pursuer by fleeing through the opening in a direction opposite to the one the pursuer came from. This deceptive dodging is the first evidence of a behaviour suggestive of intentional tactical deception among mice. As such, this deceptive behaviour may be of interest not only for rodent psychology but also, more generally, for the fields of non-human intentionality and theory of mind.

On the Use of Pitch-Based Features for Detecting Simultaneous Fear Emotion and Deception Behavior From Speech

In the last years, endowing the machine with emotional and behavioral intelligence has been one of the most challenging issues in the human–computer interaction area. In this context, this paper investigates the detection of simultaneous fear emotion and deception behavior in speech. To do so, a set of 72 pitch-based features has been investigated first to recognize fear and deception separately. Then, different feature selection techniques have been used in order to select the most relevant ones that best discriminate between fear/nonfear and deception/nondeception classes. Next, a decision-level fusion approach based on the belief theory has been proposed to infer whether fear and deception are detected simultaneously. Simulation results carried on databases dealing with fear/nonfear emotions and deception/truth behaviors have shown classification results reaching 83.33% and 72.45% as accuracy rates for fear and deception classifiers, respectively. The proposed fusion approach has revealed a correspondence between fear emotion and deception behavior in speech modality.

Cultivating Honest Character in Social Context: The Basis for Realizing Academic Integrity

Technological advances have a big impact on every line of life, including education. Everyone in the world of education can easily access information from various sources on the internet, this causes a lot of academic fraud to occur, including acts of plagiarism. GONE Theory, discovered by Jack Bologne in 1993, explains a theory about the factors that underlie someone's fraudulent or deceptive behavior. GONE is an abbreviation of Greed (greed), Opportunity (opportunity), Need (need and Exposure). The aim of this research is to determine the dominant influence between the 4 cheating factors. This type of research uses ex post facto with the population being active students in the Province Bali in 2023 who are studying under the auspices of the Ministry of Education, Research and Technology, namely from Ganesha Education University, Udayana University, Bali State Polytechnic, and the Indonesian Institute of Arts, based on data obtained from the Central Statistics Agency, there are 58,588 students (BPS, 2023). table from Krejcie and Morgan, the number of samples for a population of 50,000-75,000 is 382 (taken from N = 50,000-75,000 in Morgan's table) with an accuracy level (d2) of 95% and a significance level (z2) of 5 %. The research results show that the disclosure variable has the greatest influence on academic cheating, namely 39%. Based on these results, honest character is the best solution to create academic integrity among students.

Secure intermittent impulsive consensus control for fractional-order multiagent systems under denial-of-service and deception attacks

This article addresses the secure consensus problem of fractional-order multiagent systems (FOMASs) under denial-of-service (DoS) and deception attacks in the intermittent impulsive control framework. Unlike previous cyber attack models, the multiple attacks are considered, which the agent-to-agent communication channels and the controller-to-actuator channels are randomly subjected to deception and DoS attacks, respectively. Some random variables that follow the Bernoulli distribution are proposed, which are related to the communication channels between adjacent agents to describe deceptive attack behavior. To reduce control costs and resist cyber attacks, a novel intermittent impulsive control strategy is designed to make the FOMASs achieve consensus faster, when DoS attacks are sleeping. The designed intermittent impulsive control includes the advantages of discrete sampling control and intermittent control, which means that it can reduce the injection of erroneous data by constraining the frequency of information transmission. Furthermore, some sufficient criteria including the parameters of multiple attacks are yielded to ensure mean-square quasi-consensus of FOMASs. Finally, two examples are provided to validate the rationality for the presented security control method in this study from the perspective of numerical simulation experiments.

Deception abilities emerged in large language models

Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, but were nonexistent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can trigger misaligned deceptive behavior. GPT-4, for instance, exhibits deceptive behavior in simple test scenarios 99.16% of the time (P < 0.001). In complex second-order deception test scenarios where the aim is to mislead someone who expects to be deceived, GPT-4 resorts to deceptive behavior 71.46% of the time (P < 0.001) when augmented with chain-of-thought reasoning. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

Unveiling Deceptive Intentions: Insights from Eye Movements and Pupil Size

Recognition of deceptive intentions from the eyes has been of appealing interest in the last decades but is still unresolved. Here, we report the development of a paradigm based on the Concealed Information Test enabling the study of various kinds of deception, that is, faking and concealing. Based on a card game, we compared fixation as well as pupil behaviour while participants were instructed to fake, conceal, or tell the truth. We realized two different layouts of stimulus presentation. Fixations differed between concealing versus faking and telling the truth; pupil size additionally unveiled the object of deception. We infer that various kinds of deceptive behaviour must be carefully distinguished, and contribute to how to use gaze measures as indicators of deceptive intentions.

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.

To lie or to tell the truth? The influence of processing the opponent's feedback on the forthcoming choice.

The brain mechanisms of deceptive behavior are relatively well studied, and the key brain regions involved in its processing were established. At the same time, the brain mechanisms underlying the processes of preparation for deception are less known. We studied BOLD-signal changes during the presentation of the opponent's feedback to a previous deceptive or honest action during the computer game. The goal of the game was to mislead the opponent either by means of deception or by means of telling the truth. As a result, it was shown that several brain regions that were previously demonstrated as involved in deception execution, such as the left anterior cingulate cortex and anterior insula, also underlie processes related to deception preparation. The results obtained also allowed us to suggest that brain regions responsible for performance monitoring, intention assessment, suppression of non-selected solutions, and reward processing could be involved in shaping future action selection and preparation for deception. By shedding light on the brain mechanisms underlying deception, our study contributes to a deeper understanding of this complex cognitive process. Furthermore, it emphasizes the significance of exploring brain mechanisms governing the choice between deception and truth at various stages of decision-making.

Research on confusing responses based on ChatGPT

Recently, Artificial intelligence and Machine learning have changed the nature of scientific inquiry, with chatbots moving from rule-based technology to AI technology. Open AIs ChatGPT is a prominent AI language model that has attracted great interest and attention since its launch. To better understand its role and influence in social life, it is very necessary to know its work carefully. This paper briefly introduces the development history, current situation, and future development of the ChatGPT, discusses its popular application fields, and analyzes its pros and cons. On this basis, some problems that still exist in the application are focused on. This study uses the case analysis method to emphasize the confusing responses of ChatGPT in multiple fields, telling people that while enjoying its powerful functions, should still pay attention to its side effects and risks, the most obvious one is deceptive behavior, providing users with misleading or fabricated information may further lead to other social problems. This study speculates on the future development of ChatGPT and proposes future development directions. Generally, by rationally utilizing the functions of ChatGPT, its potential in various fields can be better released, thereby promoting the advancement of conversational AI and its transformative impact on society.

The Philosophy of Goroh Nguripi Sengkuni in a Javanese View

Sengkuni's goroh nguripi philosophy stretched throughout his life from birth to death. This article will try to: 1. describe Sengkuni's goroh nguripi philosophy which stretched throughout his life from birth to death, 2. describe Sengkuni's goroh nguripi philosophy which stretches throughout his life from birth to death from the Javanese perspective. This type of research is qualitative-interpretive, the object is the story of Sengkuni's life journey from birth to death. To obtain Sengkuni's goroh nguripi philosophy which stretches across his life from birth to death, it is carried out by means of general interpretation which is based on: consistent, coherent, systematic and tested for its truth.The result: 1. Sengkuni's goroh nguripi philosophy, which stretched throughout his life from birth to death, was a deceptive behavior that brought prosperity, but there was a party that was harmed, namely the Pandavas. 2. Sengkuni's goroh nguripi philosophy, which stretches throughout his life from birth to death, in the Javanese view, is bad, aka not good (deadly lie).

Machiavellian behavior and social-emotional functioning in middle childhood and early adolescence.

Machiavellianism is an antisocial interpersonal style involving the use of manipulative, deceptive, and coercive behaviors in the pursuit of self-interest. Although widely studied as a "dark" personality trait in adults, relatively little is known about the developmental correlates of Machiavellian tendencies earlier in life. The present study addressed this knowledge gap by examining associations between Machiavellian behavior and three theoretically relevant social-emotional domains-prosocial emotions, emotion recognition skills, and self-control-in a community sample of 7- and 11-year-old Canadian children (N = 300, 50% female). Of particular interest was the extent to which individual differences in social-emotional capacities were uniquely associated with Machiavellian behavior after controlling for aggression, a relatively well-studied antisocial behavior in childhood. Parents reported on children's Machiavellian behavior and aggression. Social-emotional correlates were assessed via parent and child reports, behavioral tasks, and physiological assessments. Machiavellian behavior and aggression were similarly associated with lower parent-reported prosocial emotions and self-control. Machiavellian (but not aggressive) behavior was associated with lower child-reported guilt (a prosocial emotion), higher resting skin conductance levels (reflecting a general tendency for higher arousal), and better anger recognition. Semipartial correlational analyses indicated that these patterns of association with Machiavellian behaviors held independent of aggression. These findings indicate that, by middle childhood, Machiavellian behavior constitutes a distinct form of antisocial conduct that is associated with a unique social-emotional risk profile. (PsycInfo Database Record (c) 2024 APA, all rights reserved).