ArticleRoyal Society open science2025
Training human super-recognizers' detection and discrimination of AI-generated faces.
Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it.British journal of psychology (London, England : 1953) · 2026Article
- Training humans to detect AI-generated faces.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Training human super-recognizers' detection and discrimination of AI-generated faces.Royal Society open science · 2025Article
- AI-generated images of familiar faces are indistinguishable from real photographs.Cognitive research: principles and implications · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative adversarial networks (GANs) can create realistic synthetic faces, which have the potential to be used for nefarious purposes. The synthetic faces produced by GANs are difficult to detect and are often judged to be more realistic than real faces. Training programmes have been developed to improve human synthetic face detection accuracy, with mixed results. Here, we investigate synthetic face detection and discrimination in super-recognizers (SRs; who have exceptional face recognition skills), and typical-ability control participants. We also devised a training procedure which sought to highlight rendering artefacts. In two different experimental designs, we found that SRs (total
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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.