# 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
An architectural image of the LSTM cell is depicted below in Figure 13. It describes how previous state memory is stored in each cell of LSTM and can be used for the current iteration for better learning and prediction. Table 6 compares deep learning models used in image tampering detection. A comparative result is displayed for models using similar evaluation parameters and publicly available datasets. RRU-Net achieves the best F-1 score in similar dataset comparisons. RRU is based on Residual propagation, which helps mitigate deep neural network problems of vanishing/exploding gradient. Table 6 shows [PERSON] et al. 's Noise Net and Late Fusion results [92]. The original literature on these networks is not discussed here. A two-stream CNN model was presented by [PERSON] et al. [108]. In one stream, the RGB feature sets of pictures were learned; in another, the DCT feature set was learned. The embeddings were afterward combined for precise categorization. ResNet50 was used by [PERSON] and [PERSON] [109] to extract features from the altered photos. The NoisePrint model inflates the manipulation characteristics in the photos before sending them on to ResNet50. [PERSON] and [PERSON] [110] proposed a deep learning CNN model using multi-scale input and several convolutional layer stages. These layers are separated into the encoder and decoder blocks. Extracted feature maps from convolutional layers with numerous levels of down sampling are concatenated in the encoder block. Similarly, upsampling and combining extracted feature maps occur in decoder blocks. Using a sigmoid activation function, the final feature map categorizes pixels into forged and non-forged. [PERSON] et al. [111] suggested a process of self-attention to locate forged areas in forged pictures. A \"Channel-Wise High Pass Filter\" block was the foundation for the self-attention module (CW-HPF). CW-HPF extracts noise features using high pass filters by correlating features across channels. A self-attention technique dubbed forgery attention is developed based on the CW-HPF to obtain rich contextual dependencies of inherent inconsistency derived from tampered areas. [PERSON] et al. [112] used a noise-based approach. After carefully analyzing the noise caused by online social networks, the authors split it into predictable and unseen noises, which are then modeled independently. Mini-Net was proposed by [PERSON] and [PERSON] [113], which employed the CNN network. [PERSON], [PERSON], [PERSON], and [PERSON] [114] proposed a deep learning CNN model image patch. A pixel in a picture is classified using a patch surrounding it, and then the CNN is used to determine if the pixel is part of the tampered area. The suggested approach accurately predicts the border pixels of the tampered region and the background picture. [PERSON] and [PERSON] [115] proposed Siteforge, a customized CNN-based deep neural network with high-class filters. [PERSON] et al. [116] used multiple layered CNN networks, ManTraNet, which could detect fake images, and their local anomaly detection network could even identify the tampered regions. Similarly, [PERSON] et al. [117] created a spatial pyramid attention network, a CNN-based network with an attention mechanism for detecting and identifying the tampered regions. But with noisy pictures, there were some false-negative cases. [PERSON] et al. [118] created an encoder and decoder-based CNN for detecting image forgery. A similar approach was also used by [PERSON] et al. [119]. A Vision Transformer, often called ViT, is a type of neural network architecture that has gained significant attention and success in computer vision. It was introduced to address image classification tasks, similar to how Convolutional Neural Networks (CNNs) have traditionally been used. ViT is unique because it relies on self-attention mechanisms, previously popular in natural language processing (NLP) tasks like machine translation. [PERSON] et al. [120] have written a survey paper on using transformers in vision classification.