通过专家引导的互学习，提升多模态假新闻检测在跨领域场景下的可靠性。
Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
- 引入三阶段协同机制，从输入到决策层逐步校准跨模态证据可信度。
- 在四个数据集上实现最优准确率，领域偏差降低最高达57.3%。
- 适合关注模型鲁棒性与公平性的多模态内容安全研究者。
多模态假新闻检测模型在跨领域泛化时表现不佳，因其倾向于依赖不可靠的证据：不平衡数据放大了特定领域的捷径，而语义不一致的图文对使跨模态证据变得不可信。本文提出专家引导的互蒸馏（EGMD），在预测流程中学习哪些证据值得信任。在输入层，输入级校准将配对一致性编码为共享增益后再融合；在表示层，专家引导的教师模型对齐领域统计分布，促使领域特异性模式集中于专用专家；在决策层，原型锚定的领域专属学生通过互学习与双通道蒸馏，继承教师的特征几何结构和校准预测，同时抑制局部领域先验。此外，我们构建了Weibo_Balanced这一领域平衡基准，以隔离数据不平衡对泛化能力的影响。在两个语言的四个数据集上，EGMD均达到最先进性能，同时将领域偏差降低最多57.3%。
原文摘要 · Abstract (English)
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%.
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