Cross-Attention-Enhanced Multimodal Fake News Detection using Autoencoder-based Fusion and Transformer-based models

With the rapid spread of information online, distinguishing between real and fake content is crucial. Fake news often manipulates both textual and visual modalities to mislead readers, posing significant challenges for detection systems. A key limitation of many existing methods lies in their inability to effectively learn a unified representation of multimodal data. In this study, we propose a new neural network architecture for fake news detection that integrates a bimodal transformer-based model with a binary classification module. Our model comprises three main components: (1) feature extraction from textual and visual inputs(2) a hybrid multimodal fusion module that combines autoencoder-based early fusion with a cross-attention mechanism to capture deep inter-modal relationships, and (3) a classifier for final fake news prediction. We evaluate our model on benchmark datasets, including Fakedit and FakeNewsNet, and demonstrate its superiority over existing methods. The experimental results show significant improvements in accuracy, precision, recall, and F1-score, validating the effectiveness and robustness of our cross-attention-enhanced multimodal framework.