Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection
This work addresses fake news detection for social security by improving multimodal analysis, though it appears incremental in building on existing cross-modal consistency approaches.
The paper tackled the problem of multimodal fake news detection by proposing a Multimodal Inverse Attention Network (MIAN) that leverages modal-specific representations and explicit discrepant features, resulting in significant outperformance over state-of-the-art methods on benchmark datasets.
Multimodal fake news detection has garnered significant attention due to its profound implications for social security. While existing approaches have contributed to understanding cross-modal consistency, they often fail to leverage modal-specific representations and explicit discrepant features. To address these limitations, we propose a Multimodal Inverse Attention Network (MIAN), a novel framework that explores intrinsic discriminative features based on news content to advance fake news detection. Specifically, MIAN introduces a hierarchical learning module that captures diverse intra-modal relationships through local-to-global and local-to-local interactions, thereby generating enhanced unimodal representations to improve the identification of fake news at the intra-modal level. Additionally, a cross-modal interaction module employs a co-attention mechanism to establish and model dependencies between the refined unimodal representations, facilitating seamless semantic integration across modalities. To explicitly extract inconsistency features, we propose an inverse attention mechanism that effectively highlights the conflicting patterns and semantic deviations introduced by fake news in both intra- and inter-modality. Extensive experiments on benchmark datasets demonstrate that MIAN significantly outperforms state-of-the-art methods, underscoring its pivotal contribution to advancing social security through enhanced multimodal fake news detection.