# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 4 Results and Discussion
### Visual Analysis of Filter Networks  
Figure 10 shows that the filter networks improved the recognition accuracy for all four positional characters on the M-dataset. There was an increase in ASR from around 85% to about 95%, with filter networks contributing approximately 10 percentage points on the M-dataset. The first and fourth characters had a relatively high ASR for the CCT from only one adjacent character, while the second and third characters were affected by both sides. Therefore, the filter networks filtered out more noise and interference, resulting in a greater boost to ASR in the second and third positions.  
As opposed to the M-dataset, where ASR improved significantly with the addition of the filter networks, there was no change in ASR in the P-dataset, and a slight decrease was found in the second character, as shown in Figure 11.  
After undergoing the filtering process illustrated in Figure 12a, it is evident that the noise level in the M-CAPTCHA images has markedly diminished. Compared with M-CAPTCHA, the reason for the lack of improvement in ASR is that the P-dataset contains less background noise and interference. The strong filter networks did not only filter out the noise but also did some damage to the characters in the original image, as shown in Figure 12b. The design of filter networks is a double-edged sword that must be balanced between noise and characters. For images that contain a lot of interference and noise, it is necessary to increase the number of layers of the filter networks. On the contrary, less noise should reduce the number.  
Figure 11: ASR with and without filter networks on the P-CAPTCHA.  
Figure 10: ASR with and without filter networks on the M-CAPTCHA.  
The losses with and without the filter networks on the M-CAPTCHA and P-CAPTCHA are shown in Figure 12(a),b, respectively. The curves in the two figures indicate that the convergence speeds increased after adding the filter networks, suggesting that the interference of the two images had been reduced, which made it easier for the model to learn the features. However, the filter networks caused overfitting to the interference on the P-dataset, which affected the characters and led to an increase in MSE.  
Therefore, the number of filter units in Adaptive CAPTCHA should be proportional to the noise level. It is recommended to integrate more filter units for noisier CAPTCHAs and fewer for less noisy ones. Figure 14 shows the AASR when the filter network is configured with different units of layers, and the best AASR on the P-dataset was obtained when the filter network is AFFN. When configuring the Deep CAPCHA with two filtering units, the AASR is better than that with four filtering units. This is because the P-dataset is a low-noise CAPTCHA dataset, and strong filtering ability may damage the characters in the CAPTCHAs. It can also be seen that the AASR of the Deep CAPTCHA without any filtering units is the lowest. Therefore, by using the fusion factor Alpha, the model can find the optimal filtering unit weights, thereby achieving better performance.  
Figure 12: Comparison of images before and after filtering: (**a**) M-CAPTCHA; (**b**) P-CAPTCHA.  
Figure 13: Loss comparison before and after filtering: (**a**) M-CAPTCHA; (**b**) P-CAPTCHA.