# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Architecture
#### 2.1.2 Encoder Network Design  
The encoder, as shown in Figure 4, accepts cover video frames and random n-bit binary messages as the inputs, and the stego frames as the outputs. 1. [label=(0)]
2. Conv-module is used sequentially, including the convolutional layer, Leaky ReLU [30] activation function, and Batch Normalization (BN) [31; 32] to extract features from the cover frames, which are sized \(C\times H\times W\) (\(C\), \(H\), and \(W\) represent the channel, height, and width of video frame, respectively), and to get \(F\)-channel features that can be utilized by later conv-modules. The kernel size of convolution layer is \(3\times 3\), the  
Figure 3: Convolutional block attention mechanism. Figure 2: The CU mask of one video frame. step is 1, and the padding is 1, in order to keep the size of the output the same as that of the input. (2) The message is reshaped to \(D\times H\times W\), concatenated in the depth dimension with \(F\)-channel features of cover frames and the CU mask, which is achieved by compressing the cover frames using VVC to form (\(F+D+1\)) dimensional feature maps, where \(D\), \(H\), and \(W\) represent the depth, height, and width of the message, respectively. (3) Inspired by the U-net [33] network structure and the different-size CU partition modes of video frames in video compression, the features are fed into three feature extraction network channels. Besides the first channel that implements feature extraction without changing the height and width of features, the other two channels conduct double and quadruple down sampling on the input feature maps, respectively, that is, the height and width are changed to one-half and one-quarter of its original size through down sampling. At the same time, the channel of extracted feature maps by the convolution module becomes \(2F\) and \(4F\), respectively. Down sampling integrates the spatial information of adjacent areas, captures the context details for feature extraction, and assists the hidden message to find appropriate features in the cover video frames. The larger the down sampling scale, the larger the spatial domain information it can integrate. At the same time, the lost fine details can be compensated by obtaining more channels of features through the subsequent convolution module. (4) CBAM is introduced into three network channels. The CA module enables the network to learn to focus on \"which\" feature maps, according to the block division of video frame content in the CU mask. Successively, the SA module enables the network to learn to focus on \"where\" features, according to the block division at different positions of the CU mask. (5) The bicubic interpolation is used to reshape the feature size of three networks into \(H\times W\), and meanwhile, the accurate location of the secret information in the cover video frames is realized. When the feature maps obtained from the down sampling of three channels are stacked together, from right to left in Figure 4, the size of the feature maps changes from small to large, similar to a pyramid. Therefore, the model is called PyraGAN (pyramid-like generative adversarial network). Based on the consideration of model parameters, the above pyramid structure, which includes the model of three feature extraction channels, is called PyraGAN_F16_3, where the feature maps variable \(F\) is set as 16. In order to reduce the complexity of the model, one feature extraction channel is removed--the blue box is shown in Figure 4. This model is named PyraGAN_F32_2, where the feature dimension \(F\) is selected as 32. (6) The three-way feature maps and the inputs of the pyramid structure (including the feature maps of cover video frame, hidden messages, and CU mask) are concatenated to get (\(8F+D+1\)) channel features. (7) A convolution module containing only convolution layer maps them into features of \(C\times H\times W\) in seize.