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In recent years, the challenge of multimodal misinformation detection (MMD) has garnered significant attention by researchers and has mainly involved the creation of annotated, weakly annotated, or synthetically generated training datasets, along with the development of various deep learning MMD models. However, the problem of unimodal bias has been overlooked, where specific patterns and biases in MMD benchmarks can result in biased or unimodal models outperforming their multimodal counterparts on an inherently multimodal task, making it difficult to assess progress. In this study, we systematically investigate and identify the presence of unimodal bias in widely used MMD benchmarks, namely VMU-Twitter and COSMOS. To address this issue, we introduce the \u201cVERification of Image-TExt pairs\u201d (VERITE) benchmark for MMD which incorporates real-world data, excludes \u201casymmetric multimodal misinformation\u201d and utilizes \u201cmodality balancing\u201d. We conduct an extensive comparative study with a transformer-based architecture that shows the ability of VERITE to effectively address unimodal bias, rendering it a robust evaluation framework for MMD. Furthermore, we introduce a new method\u2014termed Crossmodal HArd Synthetic MisAlignment (CHASMA)\u2014for generating realistic synthetic training data that preserve crossmodal relations between legitimate images and false human-written captions. By leveraging CHASMA in the training process, we observe consistent and notable improvements in predictive performance on VERITE; with a 9.2% increase in accuracy. We release our code at: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/stevejpapad\/image-text-verification\">https:\/\/github.com\/stevejpapad\/image-text-verification<\/jats:ext-link><\/jats:p>","DOI":"10.1007\/s13735-023-00312-6","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T14:02:10Z","timestamp":1704722530000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["VERITE: a Robust benchmark for multimodal misinformation detection accounting for unimodal bias"],"prefix":"10.1007","volume":"13","author":[{"given":"Stefanos-Iordanis","family":"Papadopoulos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Koutlis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Symeon","family":"Papadopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis C.","family":"Petrantonakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"312_CR1","doi-asserted-by":"crossref","unstructured":"Abdelnabi S, Hasan R, Fritz M (2022) Open-domain, content-based, multi-modal fact-checking of out-of-context images via online resources. 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