The super-recogniser advantage extends to the detection of digitally manipulated faces

Josh P. Davis, David J. Robertson, Ryan E. Jenkins, Mathias Ibsen, Robert Nichols, Martha Babbs, Christian Rathgeb, Froy Lovasdal, Kiran Raja, Christoph Busch*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Face recognition by human officials remains the predominant method of identity verification in security-critical contexts. The integrity of this process can be compromised by sophisticated fraud attacks using manipulated face images. Therefore, in this study we examine whether human observers can detect digitally manipulated passport photos, and whether super-recognisers (SRs) outperform typical recogniser controls. Using two face manipulation detection tasks (DFMD1, DFMD2), participants were asked to decide whether a ‘suspected’ passport photo had been digitally manipulated. SRs were found to significantly outperform controls; this effect was not the result of a ‘speed-accuracy trade-off’. Individual differences on tests of face identification aptitude, self-rated ability, and response times, accounted for over 20% of the variance in manipulated image detection sensitivity. Taken together, these findings show that, despite increasing sophistication in digital face manipulation techniques, there is still utility in employing human operators, particularly SRs, to detect them.
Original languageEnglish
Article numbere70053
Pages (from-to)1-13
Number of pages13
JournalApplied Cognitive Psychology
Volume39
Issue number2
DOIs
Publication statusPublished - 9 Apr 2025

Funding

This research was supported by the European Union’s Horizon 2020 research and innovation program [No. 883356; iMARS], the German Federal Ministry of Education and Research, and the Hessen State Ministry for Higher Education, Research, and the Arts within their joint support of the National Research Centre for Applied Cybersecurity [ATHENE].

Keywords

  • face image manipulation
  • face morphs
  • face recognition
  • identity fraud
  • super recognisers

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