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The growth of misinformation and re-contextualized media in social media andnews leads to an increasing need for fact-checking methods. Concurrently, theadvancement in generative models makes cheapfakes and deepfakes both easier tomake and harder to detect. In this paper, we present a novel approach usinggenerative image models to our advantage for detecting Out-of-Context (OOC) useof images-caption pairs in news. We present two new datasets with a total of$6800$ images generated using two different generative models including (1)DALL-E 2, and (2) Stable-Diffusion. We are confident that the method proposedin this paper can further research on generative models in the field ofcheapfake detection, and that the resulting datasets can be used to train andevaluate new models aimed at detecting cheapfakes. We run a preliminaryqualitative and quantitative analysis to evaluate the performance of each imagegeneration model for this task, and evaluate a handful of methods for computingimage similarity.

Detecting Out-of-Context Image-Caption Pairs in News: A Counter-Intuitive Method