# Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators
## 3 Framework of the Proposed CNN
After the training process is completed, the final values mean and variance of the BN layer are used for predicting the unseen data. The ReLU layer [33] replaces negative values with zero to improve the network performance. Merely numbers of filters are changed in other blocks; the rest of the detail is similar to block 1. After the seven blocks, the global average pooling layer is followed by a fully connected layer, softmax layer, and classification layer. As the internal statistical information details are very crucial and the size of the image is also small, therefore only one global average pooling (GAP) layer is applied to prevent further information loss in the proposed network. In steganalysis [34; 35], the global average pooling layer can enhance performance. Global average pooling is applied to achieve a single element from each feature map. The global average pooling layer increases the efficiency of the fully connected layer. It is discovered in the experimental analysis that the GAP layer can increase the detection accuracy from 1% to 3%. The GAP is applied in the end only to retain the operation fingerprints. The GAP layer also reduces the overfitting issue [36]. The detection accuracy with the GAP layer is 92.47% and is 90.01% without the GAP layer for operator sequence GAU_1.0 and MF5 with JPEG compression Q1 = 85 & Q2 = 75. Therefore, there is a benefit of more than 2% in the detection accuracy after using the GAP layer. Additionally, as shown in Figure 8, the difference between validation and the testing accuracy is much less while using the GAP layer and without the GAP layer. The overfitting issue is well tackled by the GAP layer with a performance improvement. Figure 7: Bottleneck approach and details of blocks. Experiments are also performed with multiple pooling layers, however, they lead to poor performance in the end. Therefore, a single GAP layer is considered in the experiment analysis section. In the proposed CNN, the GAP layer produces 16 features, as the last convolutional layer has 16 filters. The fully connected layer combines all of the information learned from the previous layers. The input is multiplied by the weight matrix and the bias is added. The output size of the fully connected layer is five according to our problem, which has five classes. The output of the fully connected layer is processed by the softmax function. The softmax function is assigned the probability to every class. However, the sum of all probabilities should be 1. Finally, the classification layer assigns the exclusive class according to cross-entropy loss. Weight initialization of CNN is very crucial and can affect performance considerably. Additionally, random values are taken for network initialization in the previous step. However, it is not a practical solution and the performance of the network cannot be compared due to weight initialization. [PERSON] and [PERSON] [37] suggested a weight initialization strategy to give a better performance and fast convergence. The approach is more suitable for a less deep network like our proposed CNN. The weights are initialized according to the number of inputs and hidden nodes. The filter's behavior can be understood by analyzing as in Figure 9. In the first column, two pristine images are shown and their corresponding filtered images are shown in 4 \(\times\) 4 tiles in the second and third columns. Second column images filtered from layer 6 kernels displayed the coarse details, and third column images filtered from layer 15 kernels showed the fine details. The behavior of layer kernels is changed according to the position of the layer. The information provided by layers kernels becomes coarse to fine while traversing the network from start to end. The proposed network parameters are tuned with the help of exhaustive experimental analysis. The following parameters are considered in the network design. The stochastic gradient descent (SGD) algorithm is applied to curtail the loss function.