ACHER: Availability-Conditioned Hierarchical Evidence Reasoning for Multimodal Fake News Detection

Multimodal fake news detection (MFND) identifies misinformation by jointly exploiting text, images, and their cross-modal relations, a task made increasingly hard as generative AI produces more complex cross-modal fakes. Existing methods extract veracity-discriminative signals via cross-modal alignment, consistency modeling, or feature fusion, yet typically assume sufficient corresponding evidence. In practice, low consistency either reflects conflict among existing evidence or insufficient evidence that leaves the relation unverifiable; confusing the two invites unfounded judgments. To address this, we propose the Availability-Conditioned Hierarchical Evidence Reasoner (ACHER), which conditions evidence reasoning on evidence availability: bidirectional sparse localization with null-hypothesis competition estimates it, relation reasoning proceeds only when evidence is available, and each direction yields compatible, conflicting, and unverified states. From these states, ACHER derives four complementary evidence mechanisms with state gating for joint veracity prediction. Experiments on Weibo, FineFake, and AMG show that ACHER consistently outperforms representative methods, confirming its effectiveness and generalization.