| Title | CaptchaGG: A linear graphical CAPTCHA recognition model based on CNN and RNN |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Chen, Yang, Luo, Xiaonan, Xu, Songhua, Chen, Ruiai |
| Conference Name | 2022 9th International Conference on Digital Home (ICDH) |
| Keywords | CAPTCHA, captchas, CNN, Complexity theory, composability, convolutional neural networks, feature extraction, Forestry, Human Behavior, pubcrawl, Recurrent neural networks, RNN, security, sequence modeling, Training |
| Abstract | This paper presents CaptchaGG, a model for recognizing linear graphical CAPTCHAs. As in the previous society, CAPTCHA is becoming more and more complex, but in some scenarios, complex CAPTCHA is not needed, and usually, linear graphical CAPTCHA can meet the corresponding functional scenarios, such as message boards of websites and registration of accounts with low security. The scheme is based on convolutional neural networks for feature extraction of CAPTCHAs, recurrent neural forests A neural network that is too complex will lead to problems such as difficulty in training and gradient disappearance, and too simple will lead to underfitting of the model. For the single problem of linear graphical CAPTCHA recognition, the model which has a simple architecture, extracting features by convolutional neural network, sequence modeling by recurrent neural network, and finally classification and recognition, can achieve an accuracy of 96% or more recognition at a lower complexity. |
| DOI | 10.1109/ICDH57206.2022.00034 |
| Citation Key | chen_captchagg_2022 |
CaptchaGG: A linear graphical CAPTCHA recognition model based on CNN and RNN