## 2 Related Works
### SENet Channel Attention Mechanism  
In deep learning models, the richness of extracted features increases with the number of parameters, significantly improving the model's accuracy and robustness. However, this parameter expansion can lead to information overload, negatively impacting model training and real-world performance. To address this challenge, an attention mechanism is introduced in this study. This mechanism allows the model to swiftly select high-value feature information, enabling it to accurately focus on critical information while effectively mitigating the interference of irrelevant data. Typical attention mechanisms are categorized into three main types: channel attention, spatial attention, and temporal attention. These mechanisms enable the model to assign varying weights to different positions in the input sequence, allowing it to concentrate on the most relevant parts when processing each sequence element. Accordingly, this study incorporates a SENet channel attention mechanism, which enhances the feature information learned within frames of forged videos by the attention dual-stream RNN. This approach also facilitates the effective collection of local features for the deepfake detection task, compelling the network to attend to diverse local information and thereby improving the accuracy of forgery detection. SENet Attention Optimization Network Model. The fundamental concept of SENet involves the introduction of a lightweight attention module, which comprises three primary operations: Squeeze, Excitation, and Scale. The architecture of the SENet attention mechanism is illustrated in Figure 4.  
SENet utilizes the Squeeze and Excitation operations to extract inter-channel relationships and recalibrate the convolutional input accordingly. This process involves extracting convolutional features between channels and using these features to adjust the feature maps produced by the original network, as detailed below. The Squeeze operation is represented by the function \(F_{\text{sq}}(\bullet)\) in Figure 4. This operation compresses channel features by applying global average pooling, which reduces multidimensional data to a single scalar value. This scalar represents the overall importance of the features within each channel, thereby capturing the global information for each channel. The Squeeze operation is illustrated in Equation (1):  
\[z_{c}=F_{\text{sq}}(u_{c})=\frac{1}{H\times W}\sum_{x=1}^{H}\sum_{y=1}^{W}u_{c }(x,y) \tag{1}\]  
In Equation (1), \(z_{c}\) represents the result of the Squeeze operation; \(u_{c}\) denotes the input feature map matrix; \(H\) and \(W\) indicate the height and width of the feature map, respectively; and x and y represent the rows and columns of the input matrix. The Excitation component is represented by \(F_{\text{ex}}(\bullet,W)\) in Figure 4. The Excitation operation functions as a gating mechanism to generate a feature weight for each feature channel. In this process, the features obtained from the Squeeze operation pass through a fully connected layer, which involves two fully connected operations, a ReLU activation function and a Sigmoid activation function. This design enables the model to automatically  
Figure 4: Structure of the SENet attention mechanism network. learn the importance of each channel's features. The Excitation operation is described in Equation (2):  
\[s_{c}=F_{ex}(z_{c},W)=\sigma(g(z_{c},W))=\sigma(W_{2}(\delta(W_{1}z_{c}))) \tag{2}\]  
In Equation (2), \(s_{c}\) denotes the result of the Excitation operation, \(\delta\) represents the ReLU activation function, \(\sigma\) represents the Sigmoid activation function, and \(W_{1}(. )\) and \(W_{2}(. )\) refer to the two fully connected layer operations. The Scale component is represented by \(F_{scale}(\bullet,\bullet)\) in Figure 4. This component recalibrates the feature map by multiplying the weights obtained from the Excitation operation with the features of the original channels, resulting in the weighted output features.