Evidence map›Paper›PMID 41087818›Full record

ArticleCognitive research: principles and implications2025

AI-generated images of familiar faces are indistinguishable from real photographs.

Robin S S Kramer, Alex L Jones, Daniel Fitousi, Jeremy J Tree

Abstract read
In one paragraph

Article in Cognitive research: principles and implications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Robin S S KramerSchool of Psychology, Sport Science & Wellbeing, University of Lincoln, Lincoln, UK. remarknibor@gmail.com.ORCID 0000-0001-8339-8832
Alex L JonesSchool of Psychology, Swansea University, Swansea, UK.
Daniel FitousiDepartment of Psychology, Ariel University, Ariel, Israel.
Jeremy J TreeSchool of Psychology, Swansea University, Swansea, UK.

Funding

Israel Science Foundation ISF-1498/21
6 · The paper itself

Abstract

Human users are now able to generate synthetic face images with artificial intelligence (AI) tools. Although indistinguishable from real photographs, these images have tended to feature fictional identities that do not exist in the real world. As a result, their use in applied contexts, including the spread of fake information, is similarly limited. Here, we investigated a new method for generating face images (via ChatGPT plus DALL·E) and its application to both fictional and real (in this case, celebrity) identities. Our results demonstrated that generated images of both fictional (Experiment 1) and celebrity identities (Experiment 2) could not be distinguished from real photographs. Further, providing additional real photographs for comparison during the task resulted in limited gains (Experiments 3 and 4). Finally, prior familiarity with celebrity faces produced only modest performance improvements. Therefore, new methods of detection should be explored as a matter of urgency since the latest 'off the shelf' AI tools can now generate face images of real people that are essentially undetectable as synthetic to most human observers.

Indexed as

Artificial IntelligenceAutomated Facial RecognitionFacial RecognitionRecognition, PsychologyAdultFemaleHumansMalePhotographyYoung AdultArtificial intelligenceChatGPTDeepfakesFace perception

Identifiers

PMID41087818
PMCPMC12521686

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.