Version 1
: Received: 16 April 2024 / Approved: 16 April 2024 / Online: 16 April 2024 (11:01:43 CEST)
How to cite:
Wan, X.; Johari, J.; Ruslan, F.A. Adaptive-CAPTCHA: A Text CAPTCHA Solver based on CRNN and Configurable Filter Networks. Preprints2024, 2024041050. https://doi.org/10.20944/preprints202404.1050.v1
Wan, X.; Johari, J.; Ruslan, F.A. Adaptive-CAPTCHA: A Text CAPTCHA Solver based on CRNN and Configurable Filter Networks. Preprints 2024, 2024041050. https://doi.org/10.20944/preprints202404.1050.v1
Wan, X.; Johari, J.; Ruslan, F.A. Adaptive-CAPTCHA: A Text CAPTCHA Solver based on CRNN and Configurable Filter Networks. Preprints2024, 2024041050. https://doi.org/10.20944/preprints202404.1050.v1
APA Style
Wan, X., Johari, J., & Ruslan, F.A. (2024). Adaptive-CAPTCHA: A Text CAPTCHA Solver based on CRNN and Configurable Filter Networks. Preprints. https://doi.org/10.20944/preprints202404.1050.v1
Chicago/Turabian Style
Wan, X., Juliana Johari and Fazlina Ahmat Ruslan. 2024 "Adaptive-CAPTCHA: A Text CAPTCHA Solver based on CRNN and Configurable Filter Networks" Preprints. https://doi.org/10.20944/preprints202404.1050.v1
Abstract
Text-based CAPTCHA remains the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs) are the mainstream approach for text-CAPTCHA recognition, which are widely used in CAPTCHA vulnerability assessment and data collection. However, verification code recognizers are mostly deployed on the CPU platform as part of a web crawler and security assessment, they are required to have both low complexity and high recognition accuracy. Due to the specifically designed anti-attack mechanisms like noise, interference, geometric deformation, twisting, rotation, and character adhesion in text CAPTCHAs, some characters are difficult to efficiently identify with high accuracy in these complex CAPTCHA images. This paper proposed a recognition model named Adaptive-CAPTCHA with a CRNN module and trainable and configurable filtering networks, which effectively handle the interference and learn the correlation between characters in CAPTCHAs to enhance recognition accuracy. Experimental results on two datasets of different complexity show that compared with the baseline model Deep-CAPTCHA, the number of parameters of our proposed model is reduced by about 70% and the recognition accuracy is improved by more than 10 percentage points in the two datasets. In addition, the proposed model has a faster training convergence speed.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
