# ContextGuard-LVLM: Enhancing News Veracity through Fine-grained Cross-modal Contextual Consistency Verification

arXiv: https://arxiv.org/abs/2508.06623 · HTML: https://mlsanity.com/paper/2508.06623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 26 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [On Evaluating Adversarial Robustness of Large Vision-Language Models](https://mlsanity.com/paper/2305.16934/) (2023)
- [LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models](https://mlsanity.com/paper/2306.09265/) (2023)
- [Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning](https://mlsanity.com/paper/2405.10292/) (2024)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2305.15023/) (2023)
- [X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning](https://mlsanity.com/paper/2311.18799/) (2023)
- [Thread of Thought Unraveling Chaotic Contexts](https://mlsanity.com/paper/2311.08734/) (2023)
- [A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models](https://mlsanity.com/paper/2312.12730/) (2023)
- [Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback](https://mlsanity.com/paper/2501.01377/) (2025)
- [MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration](https://mlsanity.com/paper/2506.19835/) (2025)
- [Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation](https://mlsanity.com/paper/2505.24787/) (2025)
- [LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning](https://mlsanity.com/paper/2503.15621/) (2025)
- [Cross-modal Contrastive Learning for Multimodal Fake News Detection](https://mlsanity.com/paper/2302.14057/) (2023)
- [VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization](https://mlsanity.com/paper/2402.07398/) (2024)
- [Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2503.16585/) (2025)
- [Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation](https://mlsanity.com/paper/2505.16763/) (2025)
- [SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning](https://mlsanity.com/paper/2210.03963/) (2022)
- [Verifying Cross-modal Entity Consistency in News using Vision-language Models](https://mlsanity.com/paper/2501.11403/) (2025)
- [Cross-modal Consistency Learning with Fine-grained Fusion Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2311.01807/) (2023)

## Abstract (arXiv)

The proliferation of digital news media necessitates robust methods for verifying content veracity, particularly regarding the consistency between visual and textual information. Traditional approaches often fall short in addressing the fine-grained cross-modal contextual consistency (FCCC) problem, which encompasses deeper alignment of visual narrative, emotional tone, and background information with text, beyond mere entity matching. To address this, we propose ContextGuard-LVLM, a novel framework built upon advanced Vision-Language Large Models (LVLMs) and integrating a multi-stage contextual reasoning mechanism. Our model is uniquely enhanced through reinforced or adversarial learning paradigms, enabling it to detect subtle contextual misalignments that evade zero-shot baselines. We extend and augment three established datasets (TamperedNews-Ent, News400-Ent, MMG-Ent) with new fine-grained contextual annotations, including "contextual sentiment," "visual narrative theme," and "scene-event logical coherence," and introduce a comprehensive CTXT (Contextual Coherence) entity type. Extensive experiments demonstrate that ContextGuard-LVLM consistently outperforms state-of-the-art zero-shot LVLM baselines (InstructBLIP and LLaVA 1.5) across nearly all fine-grained consistency tasks, showing significant improvements in complex logical reasoning and nuanced contextual understanding. Furthermore, our model exhibits superior robustness to subtle perturbations and a higher agreement rate with human expert judgments on challenging samples, affirming its efficacy in discerning sophisticated forms of context detachment.