## 3 Data Augmentation and Model Design
### The Modified Attention Mechanism Module  
Natural corruptions obscure forgery cues, confounding detection. CNNs inherently under-prioritize high-frequency details--deeper layers learn coarser features missing localized noise traits. Meanwhile, reduced deepfake artifacts further complicate identification. We propose contextual attention mechanisms alongside augmentation to extract corruption-resilient cues. Key discriminate details often persist locally despite interference. Our novel, multi-scale, squeeze-and-excitation module, MSE, recalibrates feature maps through dual-branch convolutions. Unlike mainstream spatial/channel attention, MSE effectively aggregates multi-scale spatial-channel dependencies. It enables mining subtle forensic traces resilient to corruption. The multi-resolution architecture further enhances representation. MSE strengthens informative features and suppresses irrelevant cues. We believe attention mechanisms are indispensable alongside augmentation for combating inevitable real-world perturbations. We humbly invite the esteemed community's feedback to advance deepfake detection robustness amidst this pivotal technological challenge collectively. Figure 5 shows the specific implementation process of the MSE module. In order to extract more spatial information from feature maps, a dual-branch convolutional network is constructed by using two scales of the convolutional kernel to process feature maps. First, we convolve the feature maps \(F(i)\) using convolution kernels with scales \(3\times 3\) and \(5\times 5\), respectively. The input channel dimension of \(F(i)\) is \(C\). The dual-branch extracts the spatial information of the feature maps to obtain the feature maps \(F_{0}\) and \(F_{1}\). The output channel dimension of each feature map is \(C^{*}/2\). Second, the multi-scale feature map \(F^{*}(i)\) is obtained by connecting \(F_{0}\) and \(F_{1}\) in the channel. And the channel of \(F^{*}(i)\) is \(C^{*}\). In order to decrease the computation of the convolution process, we use group convolution in this paper. The symbol \(G\) in the convolution operation represents the group size. Third, we use SENet to extract the attention weights on the dual-branch feature map channels. The implementation process includes \(F_{\text{sq}}\) (Squeeze) and \(F_{\text{ex}}\) (Excitation). Equation (5) depicts the implementation of \(F_{\text{sq}}\). \[g_{i}=F_{\text{sq}}(F_{i})=\frac{1}{H\times W}\sum_{j=1}^{H}\sum_{k=1}^{W}F_{i}( j,k),i=0,1 \tag{5}\]  
The squeeze operation compresses features along the spatial dimension of feature maps, turning each two-dimensional feature channel into a \(1\times 1\times C^{*}\) feature vector by global averaging pooling. This vector has a global sensitivity field to some extent. The \(F_{0}\) and \(F_{1}\) branches' statistics in channels are \(g_{0}\) and \(g_{1}\) obtained by \(F_{\text{sq}}\). Then, the \(F_{\text{ex}}\) operation is performed, as shown in Equation (6). \[\mathbf{w}_{i}=F_{\text{ex}}(g_{i},W)=\sigma(W_{i}\delta(W_{i}(g_{i}))),i=0,1 \tag{6}\]  
The excitation operation generates weights for each feature channel through the parameter \(W\). It learns to explicitly model the correlation between channels. Where \(W_{0}\) and \(W_{1}\) represent the fully connected (FC) layers that play the increasing and decreasing dimension, the symbol \(\delta\) represents the Rectified Linear Unit (ReLU), and \(\sigma\) represents the Sigmoid function. The channel weights \(\mathbf{w}_{0}\) and \(\mathbf{w}_{1}\) of the two branches are obtained through \(F_{\text{ex}}\), and then they are integrated across dimensions. Equation (7) represents the  
Figure 5: The specific implementation process of Modified Squeeze-and-Excitation(MSE). SE: Squeeze-and-Excitation Network; S: Softmax function; \(\otimes\): Element-wise product. multi-scale channel weight vector \(\mathbf{w}\) of the feature graph obtained by the cross-dimensional fusion of \(\mathbf{w}_{0}\) and \(\mathbf{w}_{1}\) without destroying the channel attention vector.