# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 5 Fake Image Detection Method--Using Neural Networks
### Convolutional Neural Network--Image Specific  
A convolutional neural network, or CNN, is a deep learning neural network component designed for processing ordered arrays of input, such as photographs. The patterns in the input image, such as lines, gradients, circles, or even eyes and faces, are very well recognized by convolutional neural networks. They can automatically learn the mapping relationship between high-dimensional data and exhibit traits like translation invariance. Because of this characteristic, convolutional neural networks are particularly effective for computer vision issues like image classification, labeling, semantic segmentation, and picture synthesis. In contrast to earlier computer vision techniques, CNNs may operate directly on a raw image and do not require prior preparation. In convolutional neural networks, many convolutional layers are stacked on top of one another, and each layer can recognize progressively complicated shapes. Three or four convolutional layers are sufficient to detect handwritten numerals, but 25 layers are required to recognize human faces. CNN uses convolutional layers to analyze input images and recognize ever-more-complex qualities like how the human visual cortex is set up. Initially, a CNN-based model was used for lexical or text-based detection for spotting fake news. CNN models were used to identify counterfeit images based on user profiles and network propagation. [PERSON] et al. [86] proposed deep learning about CNN architecture and long short-term memory (LSTM). The LSTM layer was used before the CNN layer to extract features locally and densely and produce a temporal structure from the input sequence. The training utilized videos rather than single images. The temporal characteristics were collated frame by frame, and the later relationship was established. The technique is limited to face spoofing in videos only. For images, [PERSON] et al. experimented with a novel CNN model mainly designed to restrain image content and adjusted to learning features to detect tampering. Prediction-level filters are used before passing the image to the convolutional layer [87]. These filters support suppressing the main content and allowing for the manipulated features. The model enforced weight constraint during each iteration after the filter weights had undergone stochastic gradient descent by back-propagating the errors. [PERSON] and [PERSON] [88] explicitly designed a CNN architecture for cloning and image-splicing detection applications. Unlike a regular procedure, the essential 30 spatial rich models (SRM) filter sets are used to instate the weights at the first layer. This efficiently represses the image contents' characteristics and highlights the low-level artifacts produced by the manipulating attacks. The model was tested against Columbia and CASIA image datasets. [PERSON] et al. proposed an attention-based multi-semantic CRF model for detecting picture counterfeiting [89]. To locate the tampered region, it also applied the CRF approach. The model proved impervious to noise and erosion, although it performed less accurately with JPEG-compressed pictures. The outcomes of the repeated jEPG compression were more decremented. [PERSON] et al. used a Multi-task Fully Convolutional Network (MFCN) with a Single Fully CNN. The results were not significant [90]. Two streams of FCNN were used, one for producing a surface probability map and another for an edge probability map. The result of MFCN was that it outperformed existing splicing localization algorithms and could achieve finer localization than the SFCN. The degradation in performance was observed when the images were compressed, or Gaussian noise was added. Improving on it, [PERSON] et al. [91] employed a hybrid CNN-LSTM deep learning model to differentiate features in manipulated regions of an image. It is observed that discriminative features are present at the boundary of manipulated and non-manipulated regions.