Detecting Deception and Suspicion in Dyadic Game Interactions

Abstract

In this paper we focus on detection of deception and suspicion from electrodermal activity (EDA) measured on left and right wrists during a dyadic game interaction. We aim to answer three research questions: (i) Is it possible to reliably distinguish deception from truth based on EDA measurements during a dyadic game interaction? (ii) Is it possible to reliably distinguish the state of suspicion from trust based on EDA measurements during a card game? (iii) What is the relative importance of EDA measured on left and right wrists? To answer our research questions we conducted a study in which 20 participants were playing the game Cheat in pairs with one EDA sensor placed on each of their wrists. Our experimental results show that EDA measures from left and right wrists provide more information for suspicion detection than for deception detection and that the person-dependent detection is more reliable than the person-independent detection. In particular, classifying the EDA signal with Support Vector Machine (SVM) yields accuracies of 52% and 57% for person-independent prediction of deception and suspicion respectively, and 63% and 76% for person-dependent prediction of deception and suspicion respectively. Also, we found that: (i) the optimal interval of informative EDA signal for deception detection is about 1 s while it is around 3.5 s for suspicion detection; (ii) the EDA signal relevant for deception/suspicion detection can be captured after around 3.0 seconds after a stimulus occurrence regardless of the stimulus type (deception/truthfulness/suspicion/trust); and that (iii) features extracted from EDA from both wrists are important for classification of both deception and suspicion. To the best of our knowledge, this is the first work that uses EDA data to automatically detect both deception and suspicion in a dyadic game interaction setting.

VADNet: Visual-Based Anti-Cheating Detection Network in FPS Games

“…Challenge 3: Enhancing Credibility It is crucial for anti-cheat system design to ensure accurate cheating detection without falsely identifying legitimate players as cheaters. The critical challenge lies in improving target detection and cheat classification accuracy, reducing false-positive rates to avoid negatively impacting legitimate players, and enhancing the overall credibility of the cheat detection system [14,15].…”

Decoding the Pattern Between Architecture and Emotion

“…Moreover, in an application developed for analysing EDA signals, 5s is used to create epochs of data for training ML classification (Taylor et al, 2015). In another study conducted by Ondras and Gunes (2018), various segment sizes from 0.5s to 4.5s for EDA signals are used for detecting deceptive behaviour of gamers (Ondras & Gunes, 2018). There are studies that have used longer duration for segmenting EDA signals, such as Romine et al (2019), who have used the value of 30s.…”