Detecting Out-of-Context Image-Caption Pairs in News: A
  Counter-Intuitive Method
Eivind Moholdt Sohail Ahmed Khan Duc-Tien Dang-Nguyen
The growth of misinformation and re-contextualized media in social media and
news leads to an increasing need for fact-checking methods. Concurrently, the
advancement in generative models makes cheapfakes and deepfakes both easier to
make and harder to detect. In this paper, we present a novel approach using
generative image models to our advantage for detecting Out-of-Context (OOC) use
of 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 proposed
in this paper can further research on generative models in the field of
cheapfake detection, and that the resulting datasets can be used to train and
evaluate new models aimed at detecting cheapfakes. We run a preliminary
qualitative and quantitative analysis to evaluate the performance of each image
generation model for this task, and evaluate a handful of methods for computing
image similarity.