Image-Text Out-Of-Context Detection Using Synthetic Multimodal
  Misinformation
Fatma Shalabi Huy H. Nguyen Hichem Felouat Ching-Chun Chang Isao Echizen
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.