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

Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos, Panagiotis C. Petrantonakis; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5570-5579

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 NewsCLIPpings and VERITE respectively. Nevertheless the success of MUSE relying on surface-level patterns and shortcuts without examining factuality and logical inconsistencies raises critical questions about how we define the task construct datasets collect external evidence and overall how we assess progress in the field. We release our code at: https://github.com/stevejpapad/outcontext-misinfo-progress.