AI 中文总结
针对多模态假新闻检测器跨域泛化差的问题，提出专家引导的互蒸馏方法，构建域平衡基准Weibo_Balanced，在四个数据集上实现SOTA准确率并大幅降低域偏差。
AI 中文摘要
多模态假新闻检测器跨域泛化能力差，因学习到不可靠证据：由不平衡数据放大的域特定捷径，及使跨模态证据不可靠的语义不一致图文对。本文提出专家引导的互蒸馏（EGMD），在预测流程中学习可信证据：输入级校准将配对级一致性编码为融合前的共享增益；表示级中，专家引导的教师对齐域统计，促使域特定模式集中于专用专家；决策级中，以原型为锚的域特定学生通过互学习和双通道蒸馏，继承教师的特征几何与校准预测，同时抑制局部域先验。本文还构建了域平衡基准Weibo_Balanced，以分离不平衡对泛化的影响。在两种语言的四个数据集上，EGMD达到SOTA准确率，同时将域偏差降低多达57.3%。
英文摘要
Multimodal fake news detectors often generalize poorly across domains because they learn to trust unreliable evidence: domain-specific shortcuts amplified by imbalanced data and semantically inconsistent text-image pairs that make cross-modal evidence unreliable. We propose Expert-Guided Mutual Distillation (EGMD), which learns what evidence to trust across the prediction pipeline. At the input level, input-level calibration encodes pair-level coherence as a shared gain before fusion. At the representation level, an expert-guided teacher aligns domain statistics and encourages domain-specific patterns to concentrate in specialized experts. At the decision level, prototype-anchored domain-specific students use mutual learning and dual-channel distillation to inherit the teacher's feature geometry and calibrated predictions while discouraging local domain priors. We further construct Weibo_Balanced, a domain-balanced benchmark that isolates the effect of imbalance on generalization. Across four datasets in two languages, EGMD achieves state-of-the-art accuracy while reducing domain bias by up to 57.3%.
