Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation
This work addresses misinformation detection for digital media users, but it is incremental as it builds on existing methods with a new dataset and detector.
The paper tackled the problem of detecting out-of-context misinformation in image-text pairs by using synthetic data generation to address data limitations, resulting in an efficient detector validated through experiments.
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.