## 4 Experimental Results
### Calligrapher B Tests the Effect of the Cursive Model  
For Calligrapher B's cursive script model, this paper collected two authentic works by [PERSON], with 49 characters (authentic work 1) and 84 characters (authentic work 2), respectively. For comparison, two works by different calligraphers were also collected, one with 147 characters (other work 1) and the other with 395 characters (other work 2).  
In Table 3, Authentic 1 and Authentic 2 are calligraphy works in cursive style by [PERSON], while the others are works by other calligraphers. Calligraphers are more casual when writing in cursive script than when writing regular script. Although cursive script works show a more distinct personal style, the deformation of the cursive script characters is greater, and the characteristics of the character shapes are more variable and abstract, posing greater challenges for online authentication.  
Based on the test results in Table 3, except for the MobileNetv3-L model, which failed to converge, the other models could train well. In the test for Authentic 1, CBAM-EfficientNet showed a slight improvement compared to EfficientNetv2-S, while SA-EfficientNet achieved better authentication results than the previous two models, achieving accuracies of 78.411% and 85.562% in the authentication of Authentic 1.  
In summary, the improved CBAM-EfficientNet and SA-EfficientNet models both performed well and could accurately learn the abstract features of the images from the dataset for the challenging task of distinguishing the authenticity of calligraphy works. The CBAM module had more parameters than the Self-Attention module, resulting in the overall size of the CBAM-EfficientNet model reaching 5.5 GB, while the SA-EfficientNet model size is only 891 MB. The attention structure of the Self-Attention module achieved better results with fewer parameters than CBAM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Authentic 1** & **Imitation** & **Other Work 1** & **Other Work 2** \\ \hline EfficientNetv2-S & 81.762\% & 11.098\% & 0\% & 0.011\% \\ CBAM-EfficientNet & 85.717\% & 5.556\% & 0\% & 0\% \\ SA-EfficientNet & 90.997\% & 5.556\% & 0\% & 0.006\% \\ InceptionResNetv2 & 92.584\% & 10.999\% & 0.373\% & 0\% \\ Inceptionv3 & 88.614 & 5.556\% & 0\% & 0\% \\ MobileNetv3-L & - & - & - & - \\ ResNet50 & 88.950\% & 16.660\% & 3.890\% & 0.429\% \\ MobileNet & 91.385\% & 16.674\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calligrapher B’s regular script model testing results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Authentic 1** & **Authentic 2** & **Other Work 1** & **Other Work 2** \\ \hline EfficientNetv2-S & 80.326\% & 66.867\% & 13.779\% & 0.008\% \\ CBAM-EfficientNet & 82.855\% & 68.028\% & 21.169\% & 0\% \\ SA-EfficientNet & 85.562\% & 78.411\% & 25.588\% & 0.048\% \\ InceptionResNetv2 & 84.846\% & 70.616\% & 18.885\% & 0\% \\ Inceptionv3 & 87.958\% & 67.722\% & 15.060\% & 0\% \\ MobileNetv3-L & - & - & - & - \\ ResNet50 & 86.218\% & 79.113\% & 25.254\% & 0.253\% \\ MobileNet & 80.000\% & 69.813\% & 10.448\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calligrapher B tests the effect of the cursive model.  
Among the tested models, the MobileNetV3L model is lightweight with the lowest number of parameters, and its model size is only 36 MB. When training the regular script model of Calligrapher A, there was enough data for the model to learn features, and the model was able to converge. However, when training the regular script model and cursive script model of Calligrapher B, the training data were relatively limited, and the models could not converge, making them unable to complete training and testing. Moreover, in the authentication of authentic works by Calligrapher B, the model's accuracy was lower than that of the authentic works by Calligrapher A's model. Therefore, distinguishing the authenticity of calligraphy works requires a certain number of network model parameters and sufficient training data.