AIMC Topic: Lie Detection

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Deepfake video deception detection using visual attention-based method.

Scientific reports
The key objective of producing artificial digital data is to closely mimic real data. However, because of improper use by malevolent users, the legitimacy of this kind of digital content may be under threat in society. Deepfake techniques, which repl...

Forewarned Is Forearmed: The Single- and Dual-Brain Mechanisms in Detectors from Dyads of Varying Social Distance during Deceptive Outcome Evaluation.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Preventing deception requires understanding how lie detectors process social information across social distance. Although the outcomes of such information are crucial, how detectors evaluate gains or losses from close versus distant others remains un...

Multimodal machine learning for deception detection using behavioral and physiological data.

Scientific reports
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. T...

Experimental economics for machine learning-a methodological contribution on lie detection.

PloS one
In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new techn...

An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.

Sensors (Basel, Switzerland)
In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. ...

Detecting deception with artificial intelligence: promises and perils.

Trends in cognitive sciences
Rapid advancements in artificial intelligence (AI) have driven interest in its potential application for lie detection. Unfortunately, the current approaches have primarily focused on technical aspects at the expense of a solid methodological and the...

Machine learning based approach to exam cheating detection.

PloS one
The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of dir...

Steganographer detection via a similarity accumulation graph convolutional network.

Neural networks : the official journal of the International Neural Network Society
Steganographer detection aims to identify guilty users who conceal secret information in a number of images for the purpose of covert communication in social networks. Existing steganographer detection methods focus on designing discriminative featur...

Improved semi-supervised autoencoder for deception detection.

PloS one
Existing algorithms of speech-based deception detection are severely restricted by the lack of sufficient number of labelled data. However, a large amount of easily available unlabelled data has not been utilized in reality. To solve this problem, th...