# 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
The images are passed through a basic convolutional layer at the first level to produce sixteen feature maps. One of the feature maps is passed to the LSTM layer in blocks. LSTM learns the boundary variations between different blocks and generates unique features. This helps in separating the tampered region from the non-tampered region. Now, further layers of CNN learn features from the manipulated regions. Under the Adobe Research program, [PERSON] et al. [92] employed a novel model using a two-stream Faster R-CNN network, one for RGB and the other for noise. SRM filters the extracted noise features between manipulated and authentic regions. RGB stream is designed to produce tampering feature artifacts like fabricated boundaries and acute color differences. The noise branch captures the features specific to noise using SRM filters, commonly used in steganalysis. A bilinear pooling layer joins features from both streams to further integrate these two modalities' spatial co-occurrence. The model showed slight degradation over copy-and-move tampering as the manipulated region was from the same image. However, compressed images were not taken in the experiment. Working on optimizing training time, [PERSON] et al. proposed an optimized model, LiveNet [93], based on the data randomization technique, which is like enhanced bootstrapping. In opposition to conventional CNN models, where the training set is randomly arranged once, that paper suggested continuously picking random mini-batches from the full training set at each training iteration. This led to a significant improvement in training time over the datasets. The problem of overfitting is also mitigated with this technique. This model was verified against an inter-database and cross-database containing human faces anti-spoofing data. [PERSON] et al. proposed another multi-branch framework, a coarse-to-refined convolutional neural net (C2 RNet) [94]. In the first stage, there are two cascading CNN models. The first one is Coarse-CNN (C-CNN), a VGG-16-based framework to identify the different manipulated regions in an image. The output of C-CNN is on the coarse level. Therefore, some inaccurately identified areas may be present, especially around the edge of the picture. The resultant C-CNN is then cascaded to the next Refined-CNN (R-CNN) model to train over the image features' differences. R-CNN is based on the VGG-19 framework. The technique proposes an image-level CNN against the commonly used patch-level CNN to decrease computational time. Finally, an adaptive clustering technique is suggested to produce the final detected tampered regions. Adaptive clustering has two stages, an adaptive outlier filtering and a convex full-filling stage. The model achieves good results against the CASIA and Columbia datasets, though degradation is observed when multiple attacks like compression and noise are added. Improving over BusterNet [95], a two-branch Deep Neural Network (DNN), was proposed by [PERSON] et al. for a copy-move fake detection (CFMD). It has two branches, Mini-det and Simi-Det. While the Mini-det was designed to spot tampered regions so that its feature is useful for the property; on the other hand, the Simi-Det was designed to find cloned regions and learn their features. Later, the two branches were merged to estimate pixel-level copy-move masks, distinguishing them from the authentic original image. On similar grounds, a two-stage cascading CNN model was proposed by [PERSON] et al., proposing a CNN-based architecture named Ringed Residual U-Net (RRU-Net) [96], which provides a complete image segregation system. RRU-Net aims to optimize CNN learning through the recall and reorganization process of the human cerebral cortex. To resolve the gradient degradation issue of DNN, the residual block is skipped by one layer and utilized to recall the input vector data. The residual feedback collates the input feature information to discriminate between the true and tampered areas. The RRU-Net is executed on COLUMBIA and CASIA datasets.