TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913812258488320 |
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| author | Dell'Anna, Stefano Montibeller, Andrea Boato, Giulia |
| author_facet | Dell'Anna, Stefano Montibeller, Andrea Boato, Giulia |
| contents | AI-generated synthetic media are increasingly used in real-world scenarios, often with the purpose of spreading misinformation and propaganda through social media platforms, where compression and other processing can degrade fake detection cues. Currently, many forensic tools fail to account for these in-the-wild challenges. In this work, we introduce TrueFake, a large-scale benchmarking dataset of 600,000 images including top notch generative techniques and sharing via three different social networks. This dataset allows for rigorous evaluation of state-of-the-art fake image detectors under very realistic and challenging conditions. Through extensive experimentation, we analyze how social media sharing impacts detection performance, and identify current most effective detection and training strategies. Our findings highlight the need for evaluating forensic models in conditions that mirror real-world use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20658 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks Dell'Anna, Stefano Montibeller, Andrea Boato, Giulia Multimedia Artificial Intelligence Computer Vision and Pattern Recognition AI-generated synthetic media are increasingly used in real-world scenarios, often with the purpose of spreading misinformation and propaganda through social media platforms, where compression and other processing can degrade fake detection cues. Currently, many forensic tools fail to account for these in-the-wild challenges. In this work, we introduce TrueFake, a large-scale benchmarking dataset of 600,000 images including top notch generative techniques and sharing via three different social networks. This dataset allows for rigorous evaluation of state-of-the-art fake image detectors under very realistic and challenging conditions. Through extensive experimentation, we analyze how social media sharing impacts detection performance, and identify current most effective detection and training strategies. Our findings highlight the need for evaluating forensic models in conditions that mirror real-world use. |
| title | TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks |
| topic | Multimedia Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.20658 |