AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes ReVR, a dual-path reasoning framework that constructs and applies reusable verification concepts for multimodal fake news detection and demonstrates the effectiveness of the method regarding detection performance and generalizability.
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.
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...
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.
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