RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection

• URL: http://arxiv.org/abs/2601.03981v1
• Date: Wed, 07 Jan 2026 14:52:15 GMT
• Title: RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection
• Authors: Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin, Pin-Yu Chen, Hong-Yan Huang, Shau-Yung Hsu, Yun-Nung Chen,
• Abstract summary: We present RADAR, a retrieval-augmented detector with adversarial refinement for robust fake news detection.<n>Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval.
• Score: 50.073924438848316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a retrieval-augmented detector with adversarial refinement for robust fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce verbal adversarial feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. On a fake news detection benchmark, RADAR achieves 86.98% ROC-AUC, significantly outperforming general-purpose LLMs with retrieval. Ablation studies confirm that detector-side retrieval yields the largest gains, while VAF and few-shot demonstrations provide critical signals for robust training.

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