# Deep Learning-Based Digital Image Forgery Detection System
## 3 Proposed System Architecture  
In this study, we proposed a deep learning-based approach for the identification of forged images. We proposed an architecture using ResNet50v2 as our base model, and we used the YOLO CNN weights for transfer learning. This approach enabled us to train the model with meaningful weights. We used pre-trained weights of the YOLO CNN object detection model to initialize our ResNet50v2-based proposed architecture, which saved a considerable amount of training costs, as we initialized our model with meaningful pre-trained weights. Figure 3 presents the basic architecture of ResNet50v2, in which initially batch normalization is performed, followed by an activation function and the weights being updated. Then we performed the batch normalization, ReLU activation function. After the activation function, the weights were optimized. The basic difference from the ResNet50v2 architecture is that we used pre-activation of the weight layers instead of post-activation. ResNet50v2 was developed in such a way that it removes the nonlinearity, hence clearing a path from the input to the output as a means of an identity connection. Version 2 of the ResNet module also applies the batch normalization and the activation function before the weights are multiplied. The overall proposed system is shown in Figure 4.  
Figure 3: ResNet50v2 architecture. Since the input and output dimensions are not the same, the residual block function is defined in Equation (1), as follows:  
\[y=F(x,\ \{W_{i}\})+W_{s}x \tag{1}\]  
In Equation (1), \(F\) is the residual block function, \(x\) represents the input image, and \(W\) represents the pre-trained weights of the YOLO CNN. Since the residual block function does block mapping with zero extra paddings, changing the dimensions, Resnet produces significantly better results. We also addressed the degradation problem by the utilization of a deep residual learning framework. The desired underlined mapping is fitted into the stack layers. Formally, if we represent underlined mapping as \(H(x)\), then the mapping of another nonlinear stacked layer is \(F(x):=H(x)-x\). We determined that optimizing the original is more difficult compared to residual mapping. It is easy to push zero residuals compared to a nonlinear layer stack. The formulation of \(F(x)-x\) is considered a shortcut connection. It refers to skipping one or more layers [25]. If identity mappings are generated from added layers, the information will be able to flow through the network, allowing any layer to serve as an original input and reducing training error. Transfer learning is considered an application of deep learning that enabled us to incorporate the pre-trained weights of an existing model on large data containing hundreds, if not thousands, of classes. Since these weights are pre-trained for large and challenging datasets, a high-end computing machine with GPU is required for this purpose, and it can take days or weeks to train and validate the model. The transfer learning approach reduces the cost of training a model from scratch and allows for achieving more accurate results in less time. As stated in [26], transfer learning is an approach by which we could optimize our model, which prevented us from training the model from scratch, and hence, improved the performance. Figure 5 shows the comparison using transfer learning in a CNN versus no transfer learning. From this graph, we can see that at the start, the transfer learning-based deep learning model performed better compared to training a model from the scratch. In this paper, we present a deep learning-based architecture that uses a transfer learning technique to utilize the weights of a YOLO CNN. Figure 4: ResNet50v2-based architecture for proposed system. In our proposed system, ResNet50v2 is used as a basic convolution model, which comprises five stages. With separate convolution and identity blocks, each block consists of three convolution layers, and the identity block also has three convolution layers.