CMIE : Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection (2025)

Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in visual reasoning and text generation. While previous studies have explored the application of MLLM for detecting out-of-context (OOC) misinformation, our empirical analysis reveals two persisting challenges of this paradigm. Evaluating the representative GPT-4o model on direct reasoning and evidence augmented reasoning, results indicate that MLLM struggle to capture the deeper relationships—specifically, cases in which the image and text are not directly connected but are associated through underlying semantic links. Moreover, noise in the evidence further impairs detection accuracy.To address these challenges, we propose CMIE, a novel OOC misinformation detection framework that incorporates a Coexistence Relationship Generation (CRG) strategy and an Association Scoring (AS) mechanism. CMIE identifies the underlying coexistence relationships between images and text, and selectively utilizes relevant evidence to enhance misinformation detection. Experimental results demonstrate that our approach outperforms existing methods.

## introduction the proliferation of multimodal misinformation on social media poses significant risks, including impairing public judgment, inciting panic, and causing economic losses @xcite . multimodal misinformation manifests in various forms, such as image-text mismatches @xcite @xcite and manipulated media @xcite . among image-text mismatches, outof-context (ooc) misinformation is particularly prevalent @xcite , where an unaltered * the corresponding author. image is paired with a new but misleading textual context. detecting such misinformation is challenging, as repurposing images with different textual descriptions can create seemingly strong yet deceptive correlations. previous studies @xcite @xcite typically detect ooc misinformation by training a small language model (slm) to assess the consistency between image and text representations. recently, some works @xcite @xcite attempt to verify information authenticity by retrieving external evidence from the internet. however, these methods often lack explainability, providing only a final classification label without generating clear, human-readable justifications. the advent of multimodal large language models (mllms) @xcite has driven significant advancements across various vision tasks @xcite . their advanced visual reasoning and text generation capabilities enable high-quality, human-readable expla-nations for ooc misinformation detection. additionally, their extensive world knowledge supports zero-shot transfer across diverse datasets, enhancing adaptability to novel misinformation instances. recent studies @xcite @xcite integrate retrieval-augmented generation (rag) techniques to further improve misinformation detection by incorporating external evidence. sniffer @xcite fine-tunes instructblip @xcite and uses chatgpt (openai, 2023) for explanation label generation, but as shown in figure 1 (a), it is computationally expensive and requires extensive labeled data. in contrast, lemma @xcite and muse @xcite avoid fine-tuning and consider evidence relevance during retrieval (figure 1 (b) ). however, they primarily rely on superficial lexical matching and fail to explain how retrieved evidence influences mllm's reasoning in detecting ooc misinformation. to thoroughly investigate the capabilities of mllm in ooc misinformation detection, this paper explores the following research questions: (1) how well can mllms perform in misinformation detection without fine-tuning or external evidence? (2) how does external evidence influence mllm-based misinformation detection? through our empirical analysis using the representative gpt-4o model (detailed in section 2), we find that mllms can achieve competitive performance in misinformation detection relying solely on their intrinsic knowledge, without requiring finetuning or external evidence. however, their judgments tend to be conservative, particularly when provided with image-text pairs that share similar contextual backgrounds but lack obvious inconsistencies. when external evidence is introduced, mllms often rely on superficial correlations rather than deeper semantic relationships, limiting their ability to make well-informed decisions. additionally, noisy or irrelevant evidence further constrains performance improvements. motivated by these observations, we propose cmie, an ooc misinformation detection framework designed to uncover deeper connections between image-text pairs and refine evidence selection accordingly, as illustrated in figure 1(c ). cmie introduces a coexistence relationship generation (crg) strategy that leverages mllms to identify and extract the underlying relationships between images and text. since images are expected to support the credibility of the associated text, they should share common expressive elements. these extracted relationships serve as a core validation thread, enabling a more structured approach to misinformation detection. to incorporate high-quality external evidence, cmie further incorporates an association scoring (as) mechanism that evaluates external evidence based on its deeper semantic alignment with the identified image-text relationships. rather than relying on superficial lexical matches, this mechanism ensures that only the most relevant evidence is considered, mitigating the impact of noisy or misleading information. the key contributions of this paper can be summarized as follows: ## methodology based on the conclusions drawn from the the above exploratory analysis, we introduce cmie: combining mllm insights with external evidence for explainable out-of-context misinformation detection. as illustrated in figure 4 , the framework takes multimodal news as input and produces final predictive label and explanation. in our approach, when relevant evidence is unavailable, we rely on the internal knowledge of the mllm for judgement. when evidence can be retrieved, we focus on rationally integrating the evidence into mllm's judgement process, which involves two key components: coexistence relationship generation strategy and association scoring mechanism. the first component identifies the deeper connection of image-text pair, while the second evaluates the significance of the evidence in relation to the connection. ## conclusion this paper explores and analyzes the performance of multimodal large language models in out-ofcontext misinformation detection, both using the internal knowledge and in combination with external evidence. we found that while mllm exhibit significant potential for ooc misinformation detection, it tend to overlook key validation aspects and susceptible to noise in external evidence. based on these findings, we proposed the coexistence relationship generation strategy and the association scoring mechanism, which are designed to help mllm identify deep connections of image-text pair and effectively utilize relevant evidence. experimental results demonstrate that our method outperforms baselines in most cases. our research provides a foundation for using mllm combined with external evidence for ooc misinformation detection, aiding in the development of more effective detection methods.