Similarity Over Factuality: Are we Making Progress on Multimodal Out-of-Context Misinformation Detection?

Out-of-context (OOC) misinformation poses a significant challenge in multimodal fact-checking, where images are paired with texts that misrepresent their original context to support false narratives. Recent research in evidence-based OOC detection has seen a trend towards increasingly complex architectures, incorporating Transformers, foundation models, and large language models. In this study, we introduce a simple yet robust baseline, which assesses MUltimodal SimilaritiEs (MUSE), specifically the similarity between image-text pairs and external image and text evidence. Our results demonstrate that MUSE, when used with conventional classifiers like Decision Tree, Random Forest, and Multilayer Perceptron, can compete with and even surpass the state-of-the-art on the NewsCLIPpings and VERITE datasets at less than 1 % of their computational complexity. Furthermore, integrating MUSE in our proposed “Attentive Intermediate Transformer Representations” (AITR) significantly improved performance, by 3.3% and 7.5% on NewsCLIP-pings and VERITE, respectively. Nevertheless, the success of MUSE, relying on surface-level patterns and short-cuts, without examining factuality and logical inconsistencies, raises critical questions about how we define the task, construct datasets, collect external evidence and over-all, how we assess progress in the field. We release our code at: https://github.com/stevejpapad/outcontext-misinfo-progress.