dc667414ab89eb977c7d1cbf88b80b36 imagin_a2025n11a227.pdf 8df2eecdf9c6d37523f8012ed1dcbdc54c120670 imagin_a2025n11a227.pdf 1c7ef5b078b191f089cad7db71dc8b3dfec8e94947f0b1868424ad25d4704286 imagin_a2025n11a227.pdf Title: Interpretation of AI-Generated vs. Human-Made Images Subject: AI-generated content has grown significantly in recent years. Today, AI-generated and human-made images coexist across various settings, including news media, social platforms, and beyond. However, we still know relatively little about how audiences interpret and evaluate these different types of images. The goal of this study was to examine whether image interpretation is influenced by the origin of the image (AI-generated vs. human-made). Additionally, we aimed to explore whether visual professionalization influences how images are interpreted. To this end, we presented 24 AI-generated images (produced using Midjourney, DALLE, and Firefly) and 8 human-made images to 161 participants—71 visual professionals and 90 non-professionals. Participants were asked to evaluate each image based on the following: (1) the source they believed the image originated from, (2) the level of realism, and (3) the level of credibility they attributed to it. A total of 5152 responses were collected for each question. Our results reveal that human-made images are more readily recognized as such, whereas AI-generated images are frequently misclassified as human-made. We also find that human-made images are perceived as both more realistic and more credible than AI-generated ones. We conclude that individuals are generally unable to accurately determine the source of an image, which in turn affects their assessment of its credibility. Keywords: image interpretation; AI-generated images; human-made images Author: Daniela Velásquez-Salamanca, Miguel Ángel Martín-Pascual and Celia Andreu-Sánchez Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Tue Jul 8 06:14:41 2025 CEST ModDate: Tue Jul 8 10:06:33 2025 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 777032 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- JINMPK+VnURWPalladioL Type 1 Custom yes yes yes 10 0 MCKVGZ+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 XFBZTU+URWPalladioL-Bold Type 1 Custom yes yes yes 22 0 XJXVPL+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 TPINMQ+CMSY10 Type 1 Builtin yes yes yes 32 0 ICBUDW+URWPalladioL-BoldItal Type 1 Custom yes yes yes 76 0 OFLNCM+PalatinoLinotype TrueType WinAnsi yes yes no 92 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-09-05 04:21:44 CEST RepresentationInformation: imagin_a2025n11a227.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-09-04 20:14:49 CEST Size: 777032 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 300 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Participants Destination: subsection.2.1 Item: Title: Stimuli Destination: subsection.2.2 Item: Title: Variables Destination: subsection.2.3 Children: Item: Title: Dependent Variables Destination: subsubsection.2.3.1 Item: Title: Independent Variables Destination: subsubsection.2.3.2 Item: Title: Data Acquisition Destination: subsection.2.4 Item: Title: Data Analysis Destination: subsection.2.5 Item: Title: Results Destination: section.3 Children: Item: Title: Source Identification Destination: subsection.3.1 Children: Item: Title: Human Portraits Destination: subsubsection.3.1.1 Item: Title: Landscapes Destination: subsubsection.3.1.2 Item: Title: Everyday Scenes Destination: subsubsection.3.1.3 Item: Title: Detailed Objects Destination: subsubsection.3.1.4 Item: Title: Realism Destination: subsection.3.2 Children: Item: Title: Human Portraits Destination: subsubsection.3.2.1 Item: Title: Landscapes Destination: subsubsection.3.2.2 Item: Title: Everyday Scenes Destination: subsubsection.3.2.3 Item: Title: Detailed Objects Destination: subsubsection.3.2.4 Item: Title: Credibility Destination: subsection.3.3 Children: Item: Title: Human Portraits Destination: subsubsection.3.3.1 Item: Title: Landscapes Destination: subsubsection.3.3.2 Item: Title: Everyday Scenes Destination: subsubsection.3.3.3 Item: Title: Detailed Objects Destination: subsubsection.3.3.4 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: Limitations Destination: section.6 Item: Title: Appendix A Destination: appendix.A. Item: Title: References Destination: appendix.B. Info: Title: Interpretation of AI-Generated vs. Human-Made Images Author: Daniela Velásquez-Salamanca, Miguel Ángel Martín-Pascual and Celia Andreu-Sánchez Subject: AI-generated content has grown significantly in recent years. Today, AI-generated and human-made images coexist across various settings, including news media, social platforms, and beyond. However, we still know relatively little about how audiences interpret and evaluate these different types of images. The goal of this study was to examine whether image interpretation is influenced by the origin of the image (AI-generated vs. human-made). Additionally, we aimed to explore whether visual professionalization influences how images are interpreted. To this end, we presented 24 AI-generated images (produced using Midjourney, DALLE, and Firefly) and 8 human-made images to 161 participants 71 visual professionals and 90 non-professionals. Participants were asked to evaluate each image based on the following: (1) the source they believed the image originated from, (2) the level of realism, and (3) the level of credibility they attributed to it. A total of 5152 responses were collected for each question. Our results reveal that human-made images are more readily recognized as such, whereas AI-generated images are frequently misclassified as human-made. We also find that human-made images are perceived as both more realistic and more credible than AI-generated ones. We conclude that individuals are generally unable to accurately determine the source of an image, which in turn affects their assessment of its credibility. 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