## 5 Results and Discussion
### Discussion  
The experiment results show the capability of the proposed method, especially for our main task (Exp. 7 and Exp. 10). Our approach not only correctly distinguishes the genuine and forgeries in the training and validation period but also achieved 99.96% and 94.37% accuracy in Exp. 7 and Exp. 10 testing dataset, respectively. Please be noted that in the Exp. 10 (which uses the ID:16 dataset), it is very difficult to verify if these signatures are from the same author, even through human visual comparison (due to the high intra-class variability and low inter-class variability), as shown in ID: 16 of Figure 4b,c also presented on the right side of Figure 5a.  
Since our training method is based on only a single known genuine sample, it may lead to poor performance, especially if the training data have relatively high intra-class variability. Thus, taking into account the fact that the experiment is carried out under such adverse conditions, the lower accuracy of Exp.10 is in line with expectations. The same reason can also explain why the attention saliency maps of ID No.16 in Figure 5a have more saliency areas than the background (compared with ID No.14). This is also due to the inevitable over-fitting under unfavorable training conditions. Furthermore, we can ensure the reliability of experimental results through the following three experimental designs.  
First, we divide the experimental dataset into three parts for training, validation, and testing. Each signature sample only belongs to one part. Moreover, the signatures used in the testing stage are collected from a whole new author whose samples are not present in the training and validation processes. Secondly, the experimental results of the controlled group are also very useful. In Table 4, we can observe that the accuracy drops significantly with the decreasing number of forgery samples. Also, the accuracy decreases when we use a relatively poor-performing network architecture (Vgg19). It shows that the way we use to improve the accuracy rate is reasonable. Thirdly, we visualize the CNN networks' decision. We can observe that the experimental results of our network mainly come from the signature strokes rather than other unrelated features (e.g., paper background texture).  
Finally, in order to explain the effectiveness of the proposed method, and also check whether it can be applied to different language type. We have further evaluated the model using real collection of Chinese signatures. Those signatures are collected from a genuine author and 4 forgers, and each forger contributes 4 signature samples by imitating the genuine one. All signatures are collected on A4 size white paper using the same ballpoint pen (0.1 mm black ink) and scanned by Canon UFR II scanner with 600 dpi resolution in 8-bit grayscale images. The rest of the process is basically the same as the main experiment (Exp. 7 and Exp. 10) of this study, and the result is also promising. We get the accuracy of 89.5%, FAR of 24.77% and FRR of 2.91% in this real collected Chinese dataset. Similarly, the saliency map in this case indicates that the model can effectively distinguish between genuine and forged strokes, as shown in Figure 6.  
Figure 6: Saliency maps for real collected Chinese signatures.  
However, such accuracy is still lower than our main experiments. We speculate that this is due to the complex structure of Chinese characters and accompanied by the higher intra-class variability. It may cause adverse conditions for CNN networks, especially when using only single reference signature.