Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916618189144064 |
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| author | Kamali, Negar Nakamura, Karyn Kumar, Aakriti Chatzimparmpas, Angelos Hullman, Jessica Groh, Matthew |
| author_facet | Kamali, Negar Nakamura, Karyn Kumar, Aakriti Chatzimparmpas, Angelos Hullman, Jessica Groh, Matthew |
| contents | Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generated provenance. Given the challenge to public trust in media posed by photorealistic AI-generated images, we conducted a large-scale experiment measuring human detection accuracy on 450 diffusion-model generated images and 149 real images. Based on collecting 749,828 observations and 34,675 comments from 50,444 participants, we find that scene complexity of an image, artifact types within an image, display time of an image, and human curation of AI-generated images all play significant roles in how accurately people distinguish real from AI-generated images. Additionally, we propose a taxonomy characterizing artifacts often appearing in images generated by diffusion models. Our empirical observations and taxonomy offer nuanced insights into the capabilities and limitations of diffusion models to generate photorealistic images in 2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images Kamali, Negar Nakamura, Karyn Kumar, Aakriti Chatzimparmpas, Angelos Hullman, Jessica Groh, Matthew Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generated provenance. Given the challenge to public trust in media posed by photorealistic AI-generated images, we conducted a large-scale experiment measuring human detection accuracy on 450 diffusion-model generated images and 149 real images. Based on collecting 749,828 observations and 34,675 comments from 50,444 participants, we find that scene complexity of an image, artifact types within an image, display time of an image, and human curation of AI-generated images all play significant roles in how accurately people distinguish real from AI-generated images. Additionally, we propose a taxonomy characterizing artifacts often appearing in images generated by diffusion models. Our empirical observations and taxonomy offer nuanced insights into the capabilities and limitations of diffusion models to generate photorealistic images in 2024. |
| title | Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images |
| topic | Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.11989 |