Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources - Abdelnabi, Sahar; Hasan, Rakibul; Fritz, Mario (Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online), 2022)
Misinformation is now a major problem due to its poten-tial high risks to our core democratic and societal values and orders. Out-of-context misinformation is one of the easiest and effective ways used by adversaries to spread vi-ral false stories. In this threat, a real image is re-purposed to support other narratives by misrepresenting its context and/or elements. The internet is being used as the go-to way to verify information using different sources and modali-ties. Our goal is an inspectable method that automates this time-consuming and reasoning-intensive process by fact-checking the image-caption pairing using Web evidence. To integrate evidence and cues from both modalities, we intro-duce the concept of 'multi-modal cycle-consistency check' starting from the image/caption, we gather tex-tual/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 different modalities: the caption vs. textual evidence, the image vs. visual evidence, and the image vs. caption. Our work offers the first step and bench-mark for open-domain, content-based, multi-modal fact-checking, and significantly outperforms previous baselines that did not leverage external evidence 1 1 For code, checkpoints, and dataset, check: https://s-abdelnabi.github.io/OoC-multi-modal-fc/.