## 2 Related Work
### Deep Learning Visual Model Architecture
#### 2.4.1 Convolutional Neural Network (CNN)  
In the field of computer vision, image feature extraction and classification have always been the main focus of research attention. Convolutional Neural Network (CNN) is one of the mainstream methods. It updates network parameters through the backpropagation algorithm. During the training process, it can automatically extract effective features from images and perform classification. Therefore, it is widely used in tasks such as image classification, object detection, and segmentation. Compared with the perceptron, the uniqueness of CNN lies in its processing of local regions of images through convolution kernels and the sharing of weights at different levels. The network can learn more abstract and high-level feature representations from the data. This structure makes CNN more suitable for processing image data and better able to capture the spatial locality and hierarchical structure in images. Figure 5: ROC curve and AUC area example. The blue solid line represent the EER curve. The earliest CNN structure was the LeNet model proposed by [PERSON], which is mainly used for handwritten character recognition [23]. Limited by computing resources, its performance was still not ideal, so it did not attract much attention. The AlexNet network proposed by [PERSON] et al. in 2012 achieved amazing results in the ImageNet image classification competition, successfully demonstrating the powerful ability of CNN in image recognition tasks [24]. The VGGNet network [25] improved by the University of Oxford based on AlexNet not only reduced the number of parameters but also greatly enhanced the model's performance. The residual network proposed by [PERSON] et al. [26] significantly improved the problems of gradient vanishing and explosion. The object detection algorithm RCNN proposed by [PERSON] et al. [27] combined deep learning with traditional object detection algorithms for the first time, achieving a high detection accuracy. CNN has achieved remarkable results in fields such as image segmentation [28], object tracking [29; 30], and vehicle driving [31; 32]. CNN is mainly composed of the following parts:  
1. Convolutional layer. This is an important hierarchical structure in the CNN model, mainly used to process gridded data such as images, voices, and text. The core operation is the convolution operation, which extracts features from the input data by sliding the convolution kernel. In the convolution operation, the convolution kernel slides over the input data with a certain stride. Each time, it performs a dot-product operation with a local region of the input data and sums the results to obtain a new value as an element of the output feature map. Through multiple convolution operations, the high-level features of the input data can be gradually extracted. In the convolutional layer, if the input tensor feature map is \(X\), the convolution kernel required for the convolution operation is \(W\), \(m\) and \(n\) represent the row and column indices of the convolution kernel, respectively, and the feature map \(S\) obtained after convolution can be obtained through Equation (1). \[S(i,j)=(X*W)(i,j)=\sum_{m}\sum_{n}X(i+m,j+n)\cdot W(m,n)\] (1) The convolutional layer uses multiple different convolution kernels, and each convolution kernel is used to extract different features. In addition, the convolutional layer also includes a bias term and an activation function, which are used to introduce non-linearity and increase the expressive ability of the model. Parameters such as stride and padding control the process of the convolution operation. The stride determines the distance that the convolution kernel slides on the input data, and padding adds additional values (usually 0) at the boundaries of the input data, so as to keep the size of the output feature map consistent with that of the input during the convolution operation. 2. Pooling layer.