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Despite the recent attention to DeepFakes, one of the most prevalent ways tomislead audiences on social media is the use of unaltered images in a new butfalse context. To address these challenges and support fact-checkers, wepropose a new method that automatically detects out-of-context image and textpairs. Our key insight is to leverage the grounding of image with text todistinguish out-of-context scenarios that cannot be disambiguated with languagealone. We propose a self-supervised training strategy where we only need a setof captioned images. At train time, our method learns to selectively alignindividual objects in an image with textual claims, without explicitsupervision. At test time, we check if both captions correspond to the sameobject(s) in the image but are semantically different, which allows us to makefairly accurate out-of-context predictions. Our method achieves 85%out-of-context detection accuracy. To facilitate benchmarking of this task, wecreate a large-scale dataset of 200K images with 450K textual captions from avariety of news websites, blogs, and social media posts. The dataset and sourcecode is publicly available athttps://shivangi-aneja.github.io/projects/cosmos/.

COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning