Harmony in Chaos: A Progressive Noise-Resilient Network for Robust Fake News Video Detection

Xiangzheng Kong, Zhi Zeng, Chenxi Zhu, Zihan Ma, Minnan Luo

Abstract: Short videos have become a pivotal medium for news dissemination but have also accelerated the spread of fake news. Although existing detection methods have achieved significant results, they often neglect the impact of noise in multimodal data, resulting in degraded detection accuracy and limited generalization. To address these challenges, we propose the Progressive Noise-Resilient Network (PNRN), a framework designed to adaptively mitigate noise in complex scenarios. PNRN comprises two key components: a unimodal noise-resilient module and a multimodal adaptive fusion module. The unimodal noise-resistant module leverages information bottlenecks to effectively filter modality-specific noise and strengthen feature relevance within individual modalities. The multimodal adaptive fusion module employs dynamic multi-routing and a mixture of experts to dynamically prioritize informative multimodal representations while reducing cross-modal inconsistencies. Experimental results demonstrate that PNRN significantly enhances fake news video detection performance and exhibits strong generalization capabilities in diverse and noisy social environments. Main code is available at https://anonymous.4open.science/r/PNRN-F308.

External IDs:dblp:conf/icmcs/KongZZML25