ArticleFrontiers in artificial intelligence2025
What you see is not what you get anymore: a mixed-methods approach on human perception of AI-generated images.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Bad AI-generated images can be good for teaching visual literacy in biology.Journal of microbiology & biology education · 2026Article
- Understanding the influence of AI labels on consumer psychology: the moderating role of product type.Frontiers in psychology · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of text-to-image (TTI) models has made it increasingly difficult to distinguish between AI-generated and authentic photographs. This study explores human perception and detection capabilities regarding AI-generated images of landscapes, architecture, and interiors using a mixed-methods approach. A total of 104 participants took part in an online survey, classifying 50 images (25 real, 25 AI-generated) from five leading TTI models. Alongside their classifications, participants rated their level of confidence and provided optional justifications for their choices. A quantitative analysis revealed that participants correctly identified AI-generated images in 63.7% of cases overall and notably in only 29% of cases when FLUX.1-dev was used. The hierarchical model estimated lower odds of correct detection with increasing age, while education, gender, AI-tool use, media work, and editing experience showed no significant effects. Respective confidence scores highlight calibration issues and suggest potential overconfidence in more experienced groups. The qualitative analysis of 511 textual justifications uncovered several classic visual flaws such as geometric inconsistencies, unrealistic lighting, and semantic anomalies, while simultaneously showing a shift toward tacit judgments. Participants often characterized newer outputs as 'too perfect' or faintly uncanny. Therefore, this study emphasizes the need for visual literacy and regulatory mechanisms, especially in contexts susceptible to disinformation. The findings provide insights into vulnerable groups and raise awareness of the social risks posed by hyper-realistic synthetic media.
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Registered trials
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