Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929275839447040 |
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| author | Shalabi, Fatma Nguyen, Huy H. Felouat, Hichem Chang, Ching-Chun Echizen, Isao |
| author_facet | Shalabi, Fatma Nguyen, Huy H. Felouat, Hichem Chang, Ching-Chun Echizen, Isao |
| contents | Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods. We have investigated a novel approach to Out-Of-Context detection (OOCD) that uses synthetic data generation. We created a dataset specifically designed for OOCD and developed an efficient detector for accurate classification. Our experimental findings validate the use of synthetic data generation and demonstrate its efficacy in addressing the data limitations associated with OOCD. The dataset and detector should serve as valuable resources for future research and the development of robust misinformation detection systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation Shalabi, Fatma Nguyen, Huy H. Felouat, Hichem Chang, Ching-Chun Echizen, Isao Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods. We have investigated a novel approach to Out-Of-Context detection (OOCD) that uses synthetic data generation. We created a dataset specifically designed for OOCD and developed an efficient detector for accurate classification. Our experimental findings validate the use of synthetic data generation and demonstrate its efficacy in addressing the data limitations associated with OOCD. The dataset and detector should serve as valuable resources for future research and the development of robust misinformation detection systems. |
| title | Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2403.08783 |