Abstract
The increasing realism of multimodal content has made misinformation more subtle and harder to detect, especially in news media where images are frequently paired with bilingual (e.g., Chinese-English) subtitles. Such content often includes localized image edits and cross-lingual inconsistencies that jointly distort meaning while remaining superficially plausible. We introduce BiMi, a bilingual multimodal framework that jointly performs region-level localization, cross-modal and cross-lingual consistency detection, and natural language explanation for misinformation analysis. To support generalization, BiMi integrates an online retrieval module that supplements model reasoning with up-to-date external context. We further release BiMiBench, a large-scale and comprehensive benchmark constructed by systematically editing real news images and subtitles, comprising 104,000 samples with realistic manipulations across visual and linguistic modalities. To enhance interpretability, we apply Gro