# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 5 Fake Image Detection Method--Using Neural Networks
### Multi-Modal Approach
The authors' proposed solution detects fake news without depending on other subtasks or finding similarities between modalities. It utilizes both the visual and textual vectors of an article. Bidirectional Encoder Representations from Transformers (BERT) captures contextual text features. VGG-19, a model that has been pre-trained using the ImageNet dataset, was used to learn image vectors. However, many text articles must be used to verify the model. The end-to-end network known as Multimodal Variational Autoencoder (MVAE) was proposed by [PERSON] et al. [132]. The construction of an autoencoder model was a critical task. The three main modules of the suggested model are the encoder, decoder, and classifier. The model of the encoder component employs two data streams, text and visual, to train its many characteristics. It employs VGG19 to create picture features and Bi-directional LSTM to provide text features. The encoder output is sent to a decoder, which reconstructs and decodes the original post using analogous techniques. This multi-modal variational autoencoder is fused with a classifier for marking the post as true or false. The differential autoencoder using KL divergence loss learns probabilistic hidden variables by minimizing a bound on the marginal similarity of the observed data. Finally, the fake news classifier uses this multi-modal representation generated from the bimodal variational autoencoder to mark the article as real or false. [PERSON] et al. [133] proposed the Similarity Aware Fake News (SAFE) framework. SAFE examines visual and textual information in news articles. The algorithm calculates the likelihood of erroneous reports using independent textual and visual learnings. To determine whether or not it is false in the end, it later takes into account both of these probabilities and the estimated similarity index between the text and visual content. In the first stage, an extended version of Text-CNN is used separately to generate and learn textual and visual vectors for news representation. The model also explores the generated vectors' correlation among the different processes. Then, visual and text data representations and their relevance factor are mutually learned and used to identify faux news. This model is advantageous in detecting fake news where the visual and text normally mismatch or the image is irrelevant to the text content. [PERSON], [PERSON], and [PERSON] [134] suggested a hybrid features and semantic reinforcement network (HFSRNet), an encoding and decoding-based network, for picture forgery detection. Long-short-term memory (LSTM) with resampling characteristics has been employed to record traces from the picture patches for discovering manipulation artifacts. The difference between unaltered and altered areas is further amplified by consolidating characteristics taken from spinning residual units. Then, to further include the spatial co-occurrence of these two modalities, the authors hybridize characteristics from them through a concatenation. A similar encoder and decoder-based approach was employed by [PERSON] et al. [119]. [PERSON] and [PERSON] [135] used efficientNet-B0 and RoBERTa as a multi-modal approach for detecting fake images using the image and text features. Table 7 provides the \"accuracy\" comparison between different multi-modal methods. It is observed that multi-modal methods will have different accuracy based on the dataset. The results vary because the dataset's information may have more text than images or vice versa. SAFE has better results in MediaEval, whereas SpotFake works better on Weibo. The results of att-RNN in Table 7 are taken from [PERSON] et al., and the att-RNN literature is not discussed here. Also, to our best knowledge, we could not find a multi-modal approach combining image and network propagation data.