Vision-language models (VLMs) support multimodal fake news detection (FND) by producing explicit analyses. Recent methods further improve interpretability by organizing verification knowledge into explicit concepts. However, two questions remain: how to improve the reliability and applicability of verification concepts...
Zhi-Sheng Tan, Yu-Zhou Yang, Qi-Chao Ying et al.· 0 citations
This work presents HAVE (Human-centric AI-generated Visual Evidence), a diverse human-centric dataset, and proposes PAVE, a Perception-Aware Visual Evidence framework that jointly performs authenticity prediction, visual evidence grounding, and region-aligned explanation generation.
Kun Guo, Yu-Zhou Yang, Haoyue Wang et al.· 0 citations
The proliferation of deceptive content in social networks necessitates robust Fake News Detection (FND) systems. Existing pipelines either train detectors on labeled data or leverage Large Language Models (LLMs) for their reasoning ability. However, current approaches remain either limited in generalizability or prone...
Yu-Zhou Yang, Qi-Chao Ying, Sheng Li et al.· 0 citations
SRM-FND is proposed, a self-reflective multimodal reasoning framework for short-video fake news detection that outperforms strong baselines, produces more reliable and interpretable predictions, and delivers noticeable improvements in cross-dataset performance.
Pin-Jie Xu, Yu-Zhou Yang, Zhi-Sheng Tan et al.· 0 citations
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