DATTAMM: Domain-Aware Test-Time Adaptation for Multimodal Misinformation Detection

The rapid proliferation of multimodal misinformation across diverse news categories poses unprecedented challenges to digital ecosystems, where existing detection systems exhibit critical limitations in domain adaptation and fairness. Current methods suffer from two fundamental flaws: (1) severe performance variance (>35% accuracy drop in education/science categories) due to category-specific semantic shifts; (2) systemic real/fake detection bias causing up to 68.3% false positives in legitimate content—risking suppression of factual reporting especially in high-stakes domains like public health discourse. To address these dual challenges, this paper proposes the DATTAMM (Domain-Adaptive Tensorized Multimodal Model), a novel framework integrating category-aware attention mechanisms and adversarial debiasing modules. Our approach dynamically aligns textual–visual features while suppressing domain-irrelevant noise through the following: (a) semantic disentanglement layers extracting category-invariant patterns; (b) cross-modal verification units resolving inter-modal conflicts; (c) real/fake gradient alignment regularizers. Extensive experiments on nine news categories demonstrate that the DATTAMM achieves an average F1-score of 0.854, outperforming state-of-the-art baselines by 32.7%. The model maintains consistent performance with less than 5.4% variance across categories, significantly reducing accuracy drops in education and science content where baselines degrade by over 35%. Crucially, the DATTAMM narrows the real/fake F1 gap to merely 0.017, compared to 0.243–0.547 in baseline models, while cutting false positives in high-stakes domains like health news to 5.8% versus the 38.2% baseline average. These advances lower societal costs of misclassification by 79.7%, establishing a new paradigm for robust and equitable misinformation detection in evolving information ecosystems.