TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection

• URL: http://arxiv.org/abs/2509.04448v2
• Date: Thu, 30 Oct 2025 10:58:04 GMT
• Title: TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection
• Authors: Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee,
• Abstract summary: Multimodal misinformation poses an increasing societal threat that is amplified by generative AI.<n>We observe that different distortion types share common reasoning capabilities while also requiring task-specific skills.<n>We introduce TRUST-VL, a unified and explainable vision-language model for general multimodal misinformation detection.
• Score: 23.952112817046668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal misinformation, encompassing textual, visual, and cross-modal distortions, poses an increasing societal threat that is amplified by generative AI. Existing methods typically focus on a single type of distortion and struggle to generalize to unseen scenarios. In this work, we observe that different distortion types share common reasoning capabilities while also requiring task-specific skills. We hypothesize that joint training across distortion types facilitates knowledge sharing and enhances the model's ability to generalize. To this end, we introduce TRUST-VL, a unified and explainable vision-language model for general multimodal misinformation detection. TRUST-VL incorporates a novel Question-Aware Visual Amplifier module, designed to extract task-specific visual features. To support training, we also construct TRUST-Instruct, a large-scale instruction dataset containing 198K samples featuring structured reasoning chains aligned with human fact-checking workflows. Extensive experiments on both in-domain and zero-shot benchmarks demonstrate that TRUST-VL achieves state-of-the-art performance, while also offering strong generalization and interpretability.

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