Summary of Image-text Out-of-context Detection Using Synthetic Multimodal Misinformation, by Fatma Shalabi et al.
Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation
by Fatma Shalabi, Huy H. Nguyen, Hichem Felouat, Ching-Chun Chang, Isao Echizen
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to Out-Of-Context detection (OOCD) using synthetic data generation, tackling the issue of misinformation in digital information. A dataset is designed specifically for OOCD, and an efficient detector is developed for accurate classification. Experimental results validate the effectiveness of synthetic data generation, addressing limitations associated with OOCD. The provided dataset and detector can serve as valuable resources for future research and development of robust misinformation detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps find fake news on the internet by creating a new way to check if information is true or not. They made a special set of data that tests this method, and it worked well. This means we can use this approach to detect false information more accurately. The data and tool they created will be helpful for other researchers trying to stop misinformation. |
Keywords
» Artificial intelligence » Classification » Synthetic data
