Exposing Cross-Modal Consistency for Fake News Detection in Short-Form Videos

TL;DR

This paper introduces MAGIC3, a novel fake news detection method for short videos that leverages cross-modal consistency signals across text, visuals, and audio to improve accuracy and efficiency.

Contribution

MAGIC3 is the first model to explicitly model and utilize cross-tri-modal consistency at multiple granularities for fake news detection in short videos.

Findings

MAGIC3 outperforms non-VLM baselines on FakeSV and FakeTT datasets.

The model achieves VLM-level accuracy with significantly higher throughput and lower VRAM usage.

Cross-modal consistency signals effectively distinguish real from fake videos.

Abstract

Short-form video platforms are major channels for news but also fertile ground for multimodal misinformation where each modality appears plausible alone yet cross-modal relationships are subtly inconsistent, like mismatched visuals and captions. On two benchmark datasets, FakeSV (Chinese) and FakeTT (English), we observe a clear asymmetry: real videos exhibit high text-visual but moderate text-audio consistency, while fake videos show the opposite pattern. Moreover, a single global consistency score forms an interpretable axis along which fake probability and prediction errors vary smoothly. Motivated by these observations, we present MAGIC3 (Modal-Adversarial Gated Interaction and Consistency-Centric Classifier), a detector that explicitly models and exposes cross-tri-modal consistency signals at multiple granularities. MAGIC3 combines explicit pairwise and global consistency modeling with token- and frame-level consistency signals derived from cross-modal attention, incorporates multi-style LLM rewrites to obtain style-robust text representations, and employs an uncertainty-aware classifier for selective VLM routing. Using pre-extracted features, MAGIC3 consistently outperforms the strongest non-VLM baselines on FakeSV and FakeTT. While matching VLM-level accuracy, the two-stage system achieves 18-27x higher throughput and 93% VRAM savings, offering a strong cost-performance tradeoff.