5 papers
RoE-FND: Synergizing LLMs with Experiential Learning for Effective and Generalizable Evidence-Based Fake News Detection
Yuzhou Yang, Qichao Ying, Sheng Li +3
The proliferation of deceptive content in social networks necessitates robust Fake News Detection (FND) systems. Existing pipelines either train detectors on labeled data or levera…
Subjective Multi-Bias Detection with Large Language Models
Ruiyu Li, Zhiying Zhu
In this project, we delved into the pervasive challenge of bias detection within the text content. More specifically, our focus lies on the identification of subjective bias, a typ…
Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3
The rapid evolution of generative paradigms has enabled the creation of highly realistic imagery, which escalating the risks of identity fraud and the dissemination of disinformati…
Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning
Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3
With the rapid development of deep generative models, forged facial images are massively exploited for illegal activities. Although existing synthetic face detection methods have a…
RoE-FND: A Case-Based Reasoning Approach with Dual Verification for Fake News Detection via LLMs
Yuzhou Yang, Yangming Zhou, Zhiying Zhu +3
The proliferation of deceptive content online necessitates robust Fake News Detection (FND) systems. While evidence-based approaches leverage external knowledge to verify claims, e…