Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Looks real, feels fake

Conflict detection in deepfake videos

Bibliographic Data

ID21689124
AuthorsEva M Janssen (0000-0001-9064-8473, Utrecht University, corresponding author), Yarno F Mutis (Utrecht University), Tamara Van Gog (0000-0003-3766-6255, Utrecht University)
Year2025
Volume31
Issue2
Pages237-247
Publication date2025-04-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueThinking & Reasoning (JOURNAL)
Journal identifiersISSN: 1354-6783 • E-ISSN: 1464-0708
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/13546783.2024.2391794
OpenAlexW4402747925
LanguageEN
References cited21

We investigated whether people show signs of conflict detection in both more implicit and explicit judgments about the authenticity of short video clips depicting interviews with famous American actors. If so, they would be less confident when incorrectly seeing deepfakes as authentic than when correctly seeing authentic videos as authentic. Participants (N = 128; Mage = 33.7, SD = 12.1) from the USA were recruited on Prolific. Results showed that participants were more accurate at recognising deepfakes and less accurate at recognising authentic videos when they were explicitly asked to judge if a video was authentic or deepfake compared to more implicit authenticity judgments. Interestingly, they showed signs of conflict detection both when making more implicit and explicit authenticity judgments. These findings are relevant for the literature on both conflict detection in reasoning and decision-making and on deepfake recognition, as well as for research on training people to learn to recognise deepfakes

Cognitive psychology · Epistemology · Advanced Malware Detection Techniques · Adversarial Robustness in Machine Learning · Computer Science · Evacuation and Crowd Dynamics · Philosophy · Psychology · Social Psychology

  • MorePower 6.0 for Anova with relational confidence intervals and Bayesian analysis

    Open Access•Jamie I D Campbell, Valerie A Thompson•Behavior Research Methods•2012

  • Logic, Fast and Slow

    Open Access•Wim De Neys, Gordon Pennycook•Current Directions in Psychological…•2019

  • Fitting Linear Mixed-Effects Models Using lme4

    Open Access•David M Bates, Douglas Bates et al.•Journal of Statistical Software•2015

  • Providing detection strategies to improve human detection of deepfakes

    Open Access•Klaire Somoray, Dan J Miller•Computers in Human Behavior•2023

  • From bias to sound intuiting

    Open Access•Esther Boissin, Serge Caparo et al.•Cognition•2021

  • Recognizing biased reasoning

    Open Access•Eva M Janssen, Samuël B Velinga et al.•Acta Psychologica•2021

  • “You're wrong!”

    Open Access•Eva M Janssen, Matthieu Raoelison et al.•Acta Psychologica•2020

  • The Epistemic Threat of Deepfakes

    Open Access•Don Fallis•Philosophy & Technology•2021

  • Psychological Inoculation against Misinformation

    Open Access•Cecilie S Traberg, Jon Roozenbeek et al.•The Annals of the American…•2022

  • The Liar's Dividend

    Open Access•Kaylyn Jackson Schiff, Daniel S Schiff et al.•American Political Science Review•2025

  • Truth-Default Theory (TDT)

    Open Access•Timothy R Levine•Journal of Language and Social…•2014

Citation velocityhistorical
Highly citedNo

Tools

Open DOIOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae