AI RESEARCH
Head-wise Modality Specialization within MLLMs for Robust Fake News Detection under Missing Modality
arXiv CS.CL
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ArXi:2604.09711v1 Announce Type: cross Multimodal fake news detection (MFND) aims to verify news credibility by jointly exploiting textual and visual evidence. However, real-world news dissemination frequently suffers from missing modality due to deleted images, corrupted screenshots, and similar issues. Thus, robust detection in this scenario requires preserving strong verification ability for each modality, which is challenging in MFND due to insufficient learning of the low-contribution modality and scarce unimodal annotations.