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.

翻译：社交媒体和新闻中错误信息与重新语境化媒体的增长，使得事实核查方法的需求日益增加。与此同时，生成模型的进步使得廉价伪造和深度伪造更易生成且更难检测。本文提出了一种利用生成图像模型的新方法，用于检测新闻中图像-标题对在脱语境（Out-of-Context, OOC）场景下的使用。我们构建了两个新数据集，共包含通过两种不同生成模型生成的6800张图像：（1）DALL-E 2和（2）Stable-Diffusion。我们确信，本文提出的方法可进一步推动廉价伪造检测领域中生成模型的研究，且生成的数据集可用于训练和评估旨在检测廉价伪造的新型模型。我们开展了初步的定性与定量分析，以评估每种图像生成模型在此任务中的性能，并测试了多种计算图像相似度的方法。