Currently, I am studying deepfake detection using deep learning methods. Convolution neural networks, recurrent neural networks, long-short term memory networks, and vision transformers are famous deep learning-based methods that are used in deepfake detection, as I found in my study.
I was able to find that CNNs, RNNs and LSTMs are multilayered neural networks, but I found very little about the neural network layers in a Vision Transformer. (Like a typical CNN has an input layer, pooling layer, and a fully connected layer, and finally an output layer. RNN has an input layer, multiple hidden layers and an output layer.)
So, what are the main neural network layers in a Vision Transformer?