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Misinformation is now a major problem due to its potential high risks to ourcore democratic and societal values and orders. Out-of-context misinformationis one of the easiest and effective ways used by adversaries to spread viralfalse stories. In this threat, a real image is re-purposed to support othernarratives by misrepresenting its context and/or elements. The internet isbeing used as the go-to way to verify information using different sources andmodalities. Our goal is an inspectable method that automates thistime-consuming and reasoning-intensive process by fact-checking theimage-caption pairing using Web evidence. To integrate evidence and cues fromboth modalities, we introduce the concept of 'multi-modal cycle-consistencycheck'; starting from the image/caption, we gather textual/visual evidence,which will be compared against the other paired caption/image, respectively.Moreover, we propose a novel architecture, Consistency-Checking Network (CCN),that mimics the layered human reasoning across the same and differentmodalities: the caption vs. textual evidence, the image vs. visual evidence,and the image vs. caption. Our work offers the first step and benchmark foropen-domain, content-based, multi-modal fact-checking, and significantlyoutperforms previous baselines that did not leverage external evidence.

Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources