# Leverage Boosting and Transformer on Text-Image Matching for Cheap Fakes Detection +
## 1 Introduction  
In recent years, the amount of information and news has dramatically increased due to the convenience and development of social media. However, besides the benefit of its growth, it also significantly increases the quantity and impact of misinformation on individuals and society, which is one of the most dangerous things that threaten democracy, journalism, and freedom of expression. Fake news disturbs the community on a multimedia platform and causes fatal consequences in many aspects of reality and for the ordinary lives of many people. For example, fake news affected the 2016 and 2020 U.S elections.  
Besides the spread of the amount of false information, the way of spreading misleading information to the community has also changed and evolved in many types and formations, making it more effective at and convenient for deceiving humans. For example, the enlargement and popularity of microblogging platforms such as Twitter, Facebook and Instagram has also increased the speed of spreading rumours and fake news since social media platforms are becoming more and more usual and necessary things in ordinary life for many people. Furthermore, controlling the content and veracity of posts on microblogging platforms is difficult since there is a large number of users on the standard platforms such as Facebook, Twitter and Instagram.  
The blossoming of deep learning has opened new domains and technology, one of which is deepfake [1; 2]. Deepfake has received attention from the computer vision community and is a powerful technique that can manipulate images/videos with high quality and that are hard to discriminate from unaltered ones. However, despite the usefulness and effectiveness of deepfake in swaying people's beliefs, one of the most prevalent and frequent ways of spreading disinformation is out-of-context photos, which use unaltered images in news or posts with false context.  
Cheapfakes are a type of fake news that utilizes both images and new context. The danger of cheapfakes is that they are easy and cheap to make. While deepfakes use deep learning, which takes high technology and complexity to create, cheapfakes make use of simple and non-AI techniques such as photoshop, manipulating video speed, or unaltered images/videos from different events with false context, which makes it simple to create and more common.  
Based on the MIT technology review ([[https://www.technologyreview.com/2020/12/22/1015442/cheapfakes-more-political-damage-2020-election-than-deepfakes/](https://www.technologyreview.com/2020/12/22/1015442/cheapfakes-more-political-damage-2020-election-than-deepfakes/)]([https://www.technologyreview.com/2020/12/22/1015442/cheapfakes-more-political-damage-2020-election-than-deepfakes/](https://www.technologyreview.com/2020/12/22/1015442/cheapfakes-more-political-damage-2020-election-than-deepfakes/)), accessed on 7 October 2022), in the 2020 U.S presidential election, deepfakes did not disrupt the US election, but cheapfakes did. [PERSON] [3] also warned of the dangers and explained why out-of-context photos are compelling. First, photos are usually attached to news, and people are already used to them. Secondly, photos make people faster at retrieving an image-related event, making it feel more truthful. Lastly, by using photos, posts on social media platforms will receive more attention and help spread false information.  
To meet the emerging requirements of having a good tool for cheapfakes detection and overcome the limitations of existing works, we propose several approaches that utilize multimodal representation learning techniques to overcome limitations. By combining several techniques, including text entailment, image text matching, and boosting algorithms, our methods have improved performance and assessed the performance of several methods in cheapfakes detection.