CAPTCHA Recognition And Analysis Using Custom Based CNN Model - Capsecure

CAPTCHAs are automated tests designed to distin-guish between computers and humans, attacking programs, or other computerised agents that attempt to imitate human intel-ligence. The main intent of this research is to develop a method to crack CAPTCHA using a custom based convolutional neural network (CNN) model called CAP-SECURE. The proposed model aims to distinguish or tell web sites about the weaknesses and vulnerabilities of the CAPTCHAs. The CAP-SECURE model is based on sequential CNN model and it outperforms the existing CNN architecture like VGG-16 and ALEX-net. The model has the potential to solve and explore both numerical and alphanumerical CAPTCHAs. For developing an efficient model, a dataset of 200000 CAPTCHAs has been generated to train our model. In this exposition, we study CNN based deep neural network model to meet the current challenges, and provide solutions to deal with the issues regarding CAPTCHAs. The network cracking accuracy is shown to be 94.67 percent for alpha-numerical test dataset. Compared to traditional deep learning methods, the proposed custom based model has a better recognition rate and robustness.