Structural response under factual perturbations for multimodal fake news detection

Deceptive multimodal posts often remain globally plausible because the text and image can appear mutually consistent while the veracity claim depends on a small set of fragile evidence supports. Existing multimodal fake news detectors have advanced fusion, fine-grained interaction, knowledge-enhanced reasoning, and counterfactual learning, but they mainly strengthen the representation of the observed post and rarely measure which support actually sustains the final decision. We propose SRFP, a typed structural-response framework that represents each post as a configuration of entity, background, relation, and local-visual support representations. For each support type, SRFP applies a controlled cue-specific perturbation, compares the resulting factual and support-conditioned alternative states, encodes the state shift as a response trajectory, and compresses multi-type responses into a collapse signature for prediction and mechanism analysis. SRFP obtains F1/MCC scores of 94.15%/88.69% on MR2_Chinese, 93.33%/88.99% on MR2_English, and 92.68%/83.71% on Weibo. Ablation and matched-control experiments support the importance of preserving the semantic correspondence among support type, support representation, perturbation operator, and response descriptor, while progressive support-quality analysis characterizes the robustness of the learned response. These results suggest that multimodal fake news detection benefits from observing structural response under typed support challenges in addition to building stronger static image-text representations.