<p>The development of technology and social media platforms has led to the proliferation of fake news, including the cheapfakes problem. Cheapfakes can be produced easily and spread quickly; a common type is out-of-context misinformation. It is worth investigating a proper algorithm that can efficiently detect such types of misinformation. In this work, we study a new approach to the detection problem of cheapfakes by using graphical neural networks to detect out-of-context samples using the dataset from the ICME 23 Grand Challenge on Detecting Cheapfakes. Specifically, our model utilizes scene graph matching and a language model pre-trained for the natural language inference task to solve task 1 of the challenge. We also propose effective methods to generate labeled data from an unlabeled training set of the challenge. Our proposed method achieved an F1 score of 85.69% and an accuracy score of 85.50% on the public testing set, surpassing the baseline by 4.69% and 3.60%, respectively.</p>