TL;DR
The results indicate that explicitly modeling semantic conflict as a discriminative feature effectively improves detection precision and generalization, providing a robust solution for factual verification in complex media environments.
TERN induces latent deception types from image-side multimodal features through prototype-based clustering, uses the induced assignments as structural priors for downstream veracity prediction, disentangles type-discriminative factors from semantic content, and performs type-conditioned hierarchical reasoning over text...
DAR-Lite is proposed, a serial two-stage framework that rethinks the detection pipeline through explicit decoupling of representation denoising and contextual reasoning, and achieves a favorable balance between detection performance and computational cost.
Experiments show that SMD-Net outperforms the compared content-based baselines, and suggests that the proposed framework provides an effective solution for early multimodal rumor detection when propagation-based evidence is unavailable.
The proposed context-aware multimodal reasoning approach for explainable Bengali fake news detection surpasses conventional CNN, transformer, and unimodal baselines on several performance metrics and indicates how context-based multimodal reasoning can improve the efficiency and robustness of the model, along with maki...
Multimodal fake news combining text and images has become increasingly prevalent, fueled by the rapid dissemination on social media. Existing approaches predominantly rely on supervised learning–driven small multimodal language models, yet they are constrained by the knowledge scope and logical reasoning capabilities l...
An Adaptive Binary Osprey Optimization Algorithm and Cross-modal Disentangled Fusion model (ABOOA-CDF) is proposed that effectively improves detection performance and effectiveness in feature optimization, cross-modal relation modeling, and semantic fusion.
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