# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 6 Evaluation Parameters and Datasets
### Datasets  
Only some real-world benchmark datasets are available for fake images on social media platforms. However, some well-published text and propagation-based false news databases have been released and made available to the public. Good data sets are available for face detection, but fake social media images are more than artificial faces. We here provide the well-known multimedia datasets used in fake image detection. Some are not from social media platforms, but they provide suitable datasets of tampered images. Primary datasets used for fake images are presented in Table 10. The table also provides information about where these datasets can be accessed. Some are free to use, and some are shared on a paid basis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Framework** & **MediaEval** & **Weibo** \\ \hline [PERSON] et al. [129] & att-RNN & 66.40 & 77.90 \\ \hline [PERSON] et al. [129] & EANN & 71.50 & 82.70 \\ \hline [PERSON] et al. [130] & SAME & 77.24 & 81.58 \\ \hline [PERSON] et al. [131] & SpotFake & 77.77 & 89.23 \\ \hline [PERSON] et al. [132] & MVAE & 74.50 & 82.40 \\ \hline [PERSON] et al. [133] & SAFE & 87.40 & 83.80 \\ \hline [PERSON] and [PERSON] [135] & EfficientNet + BERT & 85.30 & 81.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy comparison of multi-modal approach. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{2}{c}{**Actual Values**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Predicted Value} & Positive & True-Positive & False-Positive \\ \cline{2-3}  & Negative & False-Negative & True-Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix. * BuzzFeed: This source contains data and analysis supporting the BuzzFeed News article, _\"These Are 50 of the Biggest Fake News Hits on Facebook in 2017\"_, published on 28 December 2017;
* CASIA: Natural color image repository with realistic tampering operations, available for the public for research;
* CelebA: It contains ten thousand celebrity identities, each with twenty images. There are two hundred thousand images in total;
* COCO: The Irnia Holidays--Copydays dataset contains a set of photos that are collected explicitly from personal holidays. Each photo has suffered three kinds of artificial attacks, JPEG, cropping, and \"strong\";
* Columbia: The original images in this dataset consist of 312 images from the CalPhotos collection and 10 captured using a digital camera. The data set consists of 1845 images;
* CoMoFod: This database contains a total of 13,520 forged images. These images are a set of 260 manipulated images. Of 260 images, 200 images are in a small category, and the rest are in a large category;
* FakeNewsNet: This repository contains fake news articles while traversing the fact-check websites PolitiFact and GossipCop. These articles are then explored over the web pages. The PolitiFact section has 447 real and 336 fake news articles with images, while the GossipCop section contains 16,767 real and 1650 fake articles;
* Fakkedit: The Fakeddit is the latest and largest multi-modal dataset from the real-world social networking website Reddit. It contains over 1 million fake textual news data and over 4 lakh multi-modal samples. The multi-modal samples have text and images. It has both two-way and six-way labeling. Two-way labeling is fake and real. As Reddit collects data from micro-sites like Twitter, Facebook, Instagram, and WhatsApp, this dataset has the largest diversified dataset. In the experiment, we have selected a two-way labeling of fake vs. real. As the images are from multiple platforms, it tests the framework's robustness;
* FNC: Kaggle fake news detection challenge dataset. It has content from 244 websites and includes 12,999 news stories collected from these websites;
* MediaEval: Comprises a total of 413 images, of which 193 cases are of real images, 218 cases are of fake images, and two cases are of altered videos. These images