# 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
[PERSON] and [PERSON] [97] proposed a fusion network wherein the multiple layersof denseNet are used. To make the DNN learn fast, the network uses two major assumptions instead of learning from the entire image. The hypothesis has been that noise is observed over the edges where Splicing is applied and compression ratio variations. The FusionNet works well on pre and post-processing tampering as well. [PERSON] and [PERSON] [98] used CNN and semantic segmentation to detect image manipulations. Another suitably lightweight CNN model was proposed by [PERSON] et al. [99]. It had specific convolutional and max-pool layers after experimentation. 100. [PERSON] et al. [100] employed a hybrid model of ConvLSTM for copy-and-move detection using deep neural networks, but they did not include image splicing techniques. [PERSON] et al. [101] proposed a novel method using a slant convolutional neural network (CNN) for automatic copy-move forgery detection. In parallel, much research has also been conducted for detecting deepfakes. [PERSON] et al. [102] proposed a model common fake feature network (CFFN) designed explicitly for fake face detection generated by GAN. CFFN is built on reduced DenseNet, having a two-streamed network structure like the Siamese network to allow for pairwise information as the input. Contrastive loss is used to learn the CFFs. As the model created the fake GAN images, it tends to fail on generators using another generation method. Also, videos are not covered by this method. In contrast to previous research on fake face detection, another work proposed by [PERSON] et al. proposed a novel attuning neural network architecture named \"Fake Detection Fine-tuning Network\" (FDFtNet) [103]. The model uses a \"Fine-Tune Transformer\" (FTT), which comprises many self-attention components, which supports reducing CNN's limitation in attaining long-term dependencies. The architecture uses MobileNet block V3 to determine the picture's feature vectors through inverted residual structure and linear bottleneck. The model works well on specially trained datasets and needs more generalization. [PERSON] et al. [104] proposed a universal detector technique for finding CNN-based fake faces. Their paper stated that CNN created fake images with common systematic flaws that will never be equivalent to a realistic image. The paper discusses that images produced will always retain detectable fingerprints despite using multiple CNN generators. A suitable image classifier can learn these CNN fingerprints. The study used ResNet-50 as a classifier and the ProGAN dataset to train it. Various data augmentation variants are also used to detect post-processing tampering. Contrary to the above research, [PERSON] et al. propose removing this fingerprinting and implementing GANPrintR [105]. This CNN-based deep learning model removes the fabrication of fake faces and makes them look more natural. The architecture in the study uses an autoencoder, which first learns from the real images, and then the same learning is applied to fabricated fake faces to add extra naturalness. This is achieved by removing the GAN fingerprinting over the synthetic image. The study also verified different artificial face detection techniques like XceptionNet and steganalysis to show a significant drop in the ERR over the dataset created by this model. The model can develop robust artificial faces to improve fake face detection algorithms. In their review, [PERSON] and [PERSON] [106] specifically accounted for false pictures produced by Generative Adversarial Networks. They spoke about several deep learning techniques. Another innovative hybrid approach to identify GAN-generated deepfakes was suggested by [PERSON] et al. [107]. The CNN-LSTM-based model has shown good performance in detecting faked images. LSTM was primarily used with RNN models in detecting fake news using text or sentiments. However, replacing RNN with CNN for images has also provided good results. LSTM sort of stores memory and is used in the prediction.