From Form to Logic: Masked Reconstruction and Reasoning Distillation for Short Video Fake News Detection
Qingyan Wang, Lianwei Wu, Botao Wang, Wangkang, Yaxiong Wang Hefei University of Technology
The rapid growth of short video platforms has made multimodal fake news more prevalent. Existing detectors suffer from two major limitations: (I) global-alignment bias that overemphasizes holistic cross-modal matching and thus misses subtle, localized inconsistencies; and (II) LLM-based methods that leverage powerful generative reasoning to identify cognitive forgeries but inherently suffer from hallucinations and high inference latency. To overcome these limitations, we propose PCDD, a novel Perception-Cognition Dual-driven Detector that jointly observes the form and probes the logic for short video fake news detection. The perception stream exposes fine-grained cross-modal conflicts by amplifying localized inconsistencies into explicit discrepancies. The cognition stream transfers reasoning capabilities from LLMs to a lightweight student to mine cognitive forgeries, while reducing the risk of hallucinations and eliminating reliance on LLMs at inference. Experiments on real-world datasets show that PCDD consistently outperforms baselines, while improving interpretability and robustness in data scarcity scenarios. Our code is available at: https://github.com/SeinCore/PCDD.
尚无 AI 解读，展示原文摘要
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表