REAL: Retrieval-Augmented Prototype Alignment for Improved Fake News Video Detection

Yili Li, Jian Lang, Rongpei Hong, Qing Chen, Zhangtao Cheng, Jia Chen, Ting Zhong, Fan Zhou

Abstract: Detecting fake news videos has emerged as a critical task due to their profound implications in politics, finance, and public health. However, existing methods often fail to distinguish real videos from their subtly manipulated counterparts, resulting in suboptimal performance. To address this limitation, we propose REAL, a novel model-agnostic REtrieval-Augmented prototype-aLignment framework. REAL first introduces an LLM-driven video retriever to identify contextually relevant samples for a given target video. Subsequently, a dual-prototype aligner is carefully developed to model two distinct prototypes: one representing authentic patterns from retrieved real news videos and the other encapsulating manipulation-specific patterns from fake samples. By aligning the target video’s representations with its ground-truth prototype while distancing them from the opposing prototype, the aligner captures manipulation-aware representations capable of detecting even subtle video manipulations. Finally, these enriched representations are seamlessly integrated into existing detection models in a plug-and-play manner. Extensive experiments on three benchmarks demonstrate that REAL largely enhances the detection ability of existing methods. The code and data for reproducing the results are available at https://github.com/Jian-Lang/REAL.

External IDs:dblp:conf/icmcs/LiLHCCCZZ25