Event Consistency-aware Robust Fake News Detection

With the rapid development of short video platforms (such as Kuaishou and TikTok), these platforms have increasingly become important channels for the spread of fake news.Therefore, multi-modal fake news detection has attracted extensive attention.Existing studies mainly focus on directly integrating multi-modal information or discovering implicit clues in posts to improve detection performance.However, due to the abuse of video editing techniques, event-irrelevant segments (e.g., advertisements) are frequently mixed into videos, introducing noise information, thereby weakening models' ability to learn crucial information.Moreover, video creators often inject personal tampered information into original news content through audio modality manipulation, potentially distorting the factual. To address these challenges, we propose a novel Event Consistency-aware Robust Fake News Detection (ECR-FND) framework, comprising two key components: an Event-aware Video Denoising Learning (EVDL) and an Audio Tampering-information Capturing Module (ATCM).Specifically, the EVDL filters out the event-irrelevant segments within video modality to focus on core news events. The ATCM adaptively amplifies tampering information in audio modality, enhancing the model's capacity to detect manipulation attempts.Extensive experiments on two benchmark datasets (FakeSV and FakeTT) demonstrate ECR-FND's effectiveness.Our source code is available at https://github.com/immc-lab/ECR-FND.