How to Distinguish AI-Generated Images from Authentic Photographs
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916285024043008 |
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| author | Kamali, Negar Nakamura, Karyn Chatzimparmpas, Angelos Hullman, Jessica Groh, Matthew |
| author_facet | Kamali, Negar Nakamura, Karyn Chatzimparmpas, Angelos Hullman, Jessica Groh, Matthew |
| contents | The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between real photographs and AI-generated images. To address this problem, we designed a guide to help readers develop a more critical eye toward identifying artifacts, inconsistencies, and implausibilities that often appear in AI-generated images. The guide is organized into five categories of artifacts and implausibilities: anatomical, stylistic, functional, violations of physics, and sociocultural. For this guide, we generated 138 images with diffusion models, curated 9 images from social media, and curated 42 real photographs. These images showcase the kinds of cues that prompt suspicion towards the possibility an image is AI-generated and why it is often difficult to draw conclusions about an image's provenance without any context beyond the pixels in an image. Human-perceptible artifacts are not always present in AI-generated images, but this guide reveals artifacts and implausibilities that often emerge. By drawing attention to these kinds of artifacts and implausibilities, we aim to better equip people to distinguish AI-generated images from real photographs in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08651 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How to Distinguish AI-Generated Images from Authentic Photographs Kamali, Negar Nakamura, Karyn Chatzimparmpas, Angelos Hullman, Jessica Groh, Matthew Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between real photographs and AI-generated images. To address this problem, we designed a guide to help readers develop a more critical eye toward identifying artifacts, inconsistencies, and implausibilities that often appear in AI-generated images. The guide is organized into five categories of artifacts and implausibilities: anatomical, stylistic, functional, violations of physics, and sociocultural. For this guide, we generated 138 images with diffusion models, curated 9 images from social media, and curated 42 real photographs. These images showcase the kinds of cues that prompt suspicion towards the possibility an image is AI-generated and why it is often difficult to draw conclusions about an image's provenance without any context beyond the pixels in an image. Human-perceptible artifacts are not always present in AI-generated images, but this guide reveals artifacts and implausibilities that often emerge. By drawing attention to these kinds of artifacts and implausibilities, we aim to better equip people to distinguish AI-generated images from real photographs in the future. |
| title | How to Distinguish AI-Generated Images from Authentic Photographs |
| topic | Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.08651 |