TR-GLP: A Two-Stage Framework with Temporal Reprogramming and Residual Graph Label Propagation for Short Video Fake News Detection

The rapid growth of short video platforms has made short video fake news detection a critical security task. This task predicts news authenticity by leveraging multimodal information, including video frames, text, audio, and others. Previous works typically capture temporal cues only implicitly and suffer from passive coarse aggregation that favors global semantics, thereby obscuring subtle tampering traces. In addition, treating videos as isolated instances fails to exploit global correlations, which hinders the refinement of predictions for ambiguous cases. To address these limitations, we propose a two-stage framework with Temporal Reprogramming and Residual Graph Label Propagation (TR-GLP) for short video fake news detection. Specifically, we first employ a Temporal Reprogramming module in Stage 1 to learn temporal pattern prototypes as a reference basis for video dynamics and re-express keyframe features via cross-attention over these prototypes, where prototype attention activations reveal prototype-inconsistent temporal cues and support fine-grained forgery detection. Subsequently, we utilize a Residual Graph Label Propagation Network in Stage 2 to propagate supervision from labeled to unlabeled samples over the graph, and the residual design aggregates global context while preserving instance-level features. Experiments on FakeSV and FakeTT indicate that TR-GLP delivers superior performance compared with representative baseline methods.