arXiv:2508.12711v3 Announce Type: replace 
Abstract: The proliferation of multimodal misinformation poses growing threats to public discourse and societal trust. While Large Vision-Language Models (LVLMs) have enabled recent progress in multimodal misinformation detection (MMD), the rise of generative AI (GenAI) tools introduces a new challenge: GenAI-driven news diversity, characterized by highly varied and complex content. We show that this diversity induces multi-level drift, comprising (1) model-level misperception drift, where stylistic variations disrupt a model's internal reasoning, and (2) evidence-level drift, where expression diversity degrades the quality or relevance of retrieved external evidence. These drifts significantly degrade the robustness of current LVLM-based MMD systems. To systematically study this problem, we introduce DriftBench, a large-scale benchmark comprising 16,000 news instances across six categories of diversification. We design three evaluation tasks: (1) robustness of truth verification under multi-level drift; (2) susceptibility to adversarial evidence contamination generated by GenAI; and (3) analysis of reasoning consistency across diverse inputs. Experiments with six state-of-the-art LVLM-based detectors show substantial performance drops (average F1 -14.8%) and increasingly unstable reasoning traces, with even more severe failures under adversarial evidence injection. Our findings uncover fundamental vulnerabilities in existing MMD systems and suggest an urgent need for more resilient approaches in the GenAI era.

أدى ظهور أدوات الذكاء الاصطناعي التوليدية (GenAI) إلى تحديات كبيرة لأنظمة الكشف عن المعلومات المضللة المعتمدة على نماذج اللغة والرؤية الكبيرة (LVLM). تؤدي هذه التنوعات في محتوى الأخبار إلى انحرافات متعددة المستويات، تؤثر على كل من تفكير النموذج وملاءمة الأدلة الخارجية. تم تقديم معيار جديد، DriftBench، لدراسة هذه المشكلات بشكل منهجي، ويتضمن 16,000 حالة أخبار عبر ست فئات من التنوع.

El auge de las herramientas de inteligencia artificial generativa (GenAI) ha introducido desafíos significativos para los sistemas de detección de desinformación basados en Modelos de Lenguaje y Visión de Gran Escala (LVLM). Esta diversidad en el contenido de noticias conduce a un desplazamiento a múltiples niveles, afectando tanto el razonamiento del modelo como la relevancia de la evidencia externa. Se ha introducido un nuevo benchmark, DriftBench, para estudiar sistemáticamente estos problemas, que comprende 16,000 instancias de noticias en seis categorías de diversificación.

L'essor des outils d'intelligence artificielle générative (GenAI) a introduit des défis significatifs pour les systèmes de détection de désinformation basés sur des modèles de vision-langage de grande taille (LVLM). Cette diversité dans le contenu des nouvelles entraîne un dérive à plusieurs niveaux, affectant à la fois le raisonnement des modèles et la pertinence des preuves externes. Un nouveau benchmark, DriftBench, a été introduit pour étudier systématiquement ces problèmes, comprenant 16 000 instances de nouvelles à travers six catégories de diversification.

The rise of generative AI (GenAI) tools has introduced significant challenges to misinformation detection systems based on Large Vision-Language Models (LVLMs). This diversity in news content leads to multi-level drift, affecting both model reasoning and the relevance of external evidence. A new benchmark, DriftBench, has been introduced to systematically study these issues, comprising 16,000 news instances across six diversification categories.

Drifting Away from Truth: GenAI-Driven News Diversity Challenges LVLM-Based Misinformation Detection

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Drifting Away from Truth: GenAI-Driven News Diversity Challenges LVLM-Based Misinformation Detection

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