Core Concepts
Using synthetic data generation for Out-Of-Context detection improves accuracy and reliability in identifying misinformation.
Abstract
The article discusses the challenges posed by misinformation, particularly in the form of out-of-context (OOC) content. It highlights the prevalence of multimodal misinformation, such as images and texts, and the deceptive nature of OOC content. The need for efficient detection methods to combat misinformation is emphasized. The authors propose a novel approach that leverages synthetic data generation for OOC detection. By creating a dataset specifically designed for OOC tasks and developing an efficient detector, they aim to address the limitations associated with current detection methods. The use of synthetic data enhances the diversity and complexity of training data, improving the detector's ability to identify instances of information deviating from their context. The proposed approach also focuses on explainability by generating a synthetic multimodal dataset to aid in understanding the reasoning behind detections. Additionally, a detector leveraging machine learning algorithms is developed to accurately identify OOC multimodal information.
Stats
"Our experimental findings validate the use of synthetic data generation."
"Dataset contains 85K balanced pristine and falsified examples."
"Classification accuracy rate achieved was 68%."
Quotes
"Misinformation has become a major challenge in the era of increasing digital information."
"Generating synthetic data expands diversity and complexity of training data."
"Our proposed approach resulted in the highest accuracy among approaches compared."