# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 5 Fake Image Detection Method--Using Neural Networks
### Multi-Modal Approach  
Social media fake news consists of multiple entities like texts, images, videos, audio, links, etc. Sometimes, various objects are combined to propagate fake news, like text over images or text in comments with irrelevant images. Thus, detecting fake images based on the image has some gaps. The efficiency of fake news detection solely based on image analysis only sometimes yields very high accuracy. There are fair chances that the images used in fake news are real and untampered, but the text or audio content is either irrelevant or contains false information. To overcome this problem, researchers have started applying a multi-modal approach. Besides the image, other content-based features are also considered for detection in the multi-modal approach. Multiple information is received from various streams at the end to classify so that the multi-modal architecture will require a fusion classifier. Some multi-modal approaches are shared below. A multi-modal framework using text and image has performed better than other multi-modal features [126; 127]. To combine text and images, [PERSON] et al. created the Text and Image information-based Convolutional Neural Network (TI-CNN) model [128]. By projecting the latent and explicit vectors into integrated vector space, learning the TI-CNN model is simultaneously based on image and text data. In addition to the natural features, the model uniquely employs two parallel CNNs to extract hidden features from visual and textual information. Latent and explicit vectors are projected into an integrated vector space to produce a new presentation of visuals and texts. Finally, the model recommends fusing visual and textual representations concurrently to detect faux news. Event Adversarial Neural Networks (EANN) were suggested by [PERSON] et al. [129] to identify false news, gather features independent of the event, and support fake news detection on newly emerging events. The architecture consists of the multi-modal feature extractor, the false news detector, and the event discriminator. Generating visual and textual features from postings is the primary task of the multi-modal feature extractor. Predicting whether a message is true or false is the aim of the fake news detector. An event discriminator's task is to remove event-specific features while maintaining event-invariant features. Sentiment-aware multi-modal Embedding (SAME) [130] incorporates users' hidden opinions from their comments into a single deep multi-modal embedding framework as a novel method to detect fake news. The many elements of fake news, such as the name of the publisher, user profiles, and text and image content, are managed by several networks. The adversarial method then educates semantically meaningful spaces for each data modality in the following phase. The model defines a special regularization loss in the final stage to reduce the distance between relevant pair embedding. The SpotFake framework was introduced by [PERSON] et al. [131] to eliminate sub-task dependencies \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Framework** & **Columbia** & **CASIA 1.0** & **CASIA 2.0** & **NIST 16** \\ \hline [PERSON] et al. [91] & CNN + LSTM & - & - & - & 0.764 \\ \hline [PERSON] et al. [90] & MFCN & 0.611 & 0.541 & - & 0.571 \\ \hline [PERSON] et al. [92] & FRCNN (RBG N) & 0.697 & 0.408 & - & 0.722 \\ \hline [PERSON] et al. [94] & C-CNN + R-CNN (C2 RNet) & 0.695 & - & 0.675 & - \\ \hline Noise Net [92] & FRNN + SRM Filter & 0.705 & 0.283 & - & - \\ \hline Late Fusion [92] & Fusion FRNN & 0.681 & 0.397 & - & - \\ \hline [PERSON] et al. [95] & Buster Net & - & - & 0.759 & - \\ \hline [PERSON] et al. [96] & RRU-Net & 0.915 & - & 0.841 & - \\ \hline [PERSON] et al. [119] & False-Unet & - & 0.736 & 0.695 & 0.638 \\ \hline [PERSON] et al. [122] & Vision Transformer & - & - & 0.620 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F1-score comparison of deep learning approach. like event discrimination.