Develop an Artificial Intelligence Model Solution to Refine CAPTCHA

Abstract

Completely Automated Public Turing tests to narrate Computers and Humans Apart (CAPTCHAs) are established for protection purposes, but their growing complicatedness frequently hampers consumer occurrence. This project presents a Machine Learning model to refine CAPTCHA by reinforcing protection while asserting approachability. The model influences deep education methods, particularly Convolutional Neural Networks and Recurrent Neural Networks to analyse CAPTCHA patterns, discover proneness, and improve their design. A fruitful approach utilizing Generative Adversarial Networks guarantees CAPTCHAs remain opposing to computerized solvers while being handy. Additionally, Optical Character Recognition models are used to judge CAPTCHA strength and upgrade human readability. The projected resolution aims to balance protection and utility by underrating dishonest contradiction while guaranteeing elasticity against advanced bots. The model is prepared on a various dataset of CAPTCHAs to boost changeability. This approach improves confirmation systems, providing a secure still approachable proof design across mathematical podiums.

Introduction

The widespread use of the Internet, driven by affordable devices and fast data subscriptions, has increased the need for security against automated bots that exploit online services. While CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) are widely used to distinguish humans from bots and protect resources, advances in artificial intelligence, especially machine learning (ML) and deep learning techniques like Convolutional Neural Networks (CNNs), have made it easier to break many CAPTCHA systems.

CAPTCHAs come in various types—text-based, image-based, audio-based, and math-based—with Optical Character Recognition (OCR) and non-OCR schemes. Text-based CAPTCHAs involve distorted characters, while image CAPTCHAs require users to identify specific images. Audio CAPTCHAs help visually impaired users but face challenges from automatic speech recognition systems.

The research aims to build and evaluate a machine learning model capable of breaking CAPTCHAs to understand their vulnerabilities and improve future CAPTCHA designs and overall cybersecurity. The methodology involves collecting datasets (from Kaggle and Google reCAPTCHA), preprocessing images to reduce noise and enhance features, and training models using techniques like data augmentation and adversarial training.

Literature surveys show that while CAPTCHA-breaking methods have evolved, many still face challenges with distorted or complex CAPTCHAs. The study also emphasizes ethical considerations regarding the development of CAPTCHA-breaking technology.

The goal is to contribute to better CAPTCHA designs and promote advanced security mechanisms, including biometrics and multi-factor authentication, to counter the growing threat of automated attacks.

Conclusion

This paper grown a CNN-located machine learning model to break quotation-located CAPTCHAs, achieving 95% veracity on a various dataset. The study unprotected vulnerabilities in established CAPTCHA plans, highlighting the need for more secure options. However, challenges wait, specifically in handling well crooked or overlapping figures. Future work will devote effort to something enhancing the model\'s strength through opposing preparation, making it more resilient against developing attack procedures. Additionally, exploring figure-located and behavioural CAPTCHAs can supply more secure alternatives to usual document-based structures. Reducing computational complicatedness is likewise crucial to guarantee physical-time accomplishment outside embarrassing accuracy. These progresses will cause the development of more powerful CAPTCHA means fit countering cosmopolitan robotic attacks effectively.

References

[1] Noshina Tariq, Muhammad Asim, Farrukh Aslam Khan, Thar Baker, Umair Khalid, and Abdelouahid Derhab. A blockchain-based multi-mobile code-driven trust mechanism for detecting internal attacks in internet of things. Sensors, 2021. [2] Abdelouahid Derhab, Rahaf Alawwad, Khawlah Dehwah, Noshina Tariq, Farrukh Aslam Khan, and Jalal Al-Muhtadi. Tweet-based bot detection using big data analytics [3] Nitirat Tanthavech and Apichaya Nimkoompai. Captcha: Impact of website security on user experience. In proceedingsof the 2019 4th International Conference on Intelligent Information Technology, pages 2019. [4] Yang-Wai Chow, Willy Susilo, and Pairat Thorncharoensri. Captcha design and security issues. In Advances in Cyber Security: Principles, Techniques, and Applications, pages Springer, 2019. [5] Temur ul Hassan, Muhammad Asim, Thar Baker, Jawad Hassan, and Noshina Tariq. Ctrust-rpl: A control layer-based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks-based interne of things applications. Transactions on Emerging Telecommunications Technologies, 2021. [6] Zhiyou Ouyang, Xu Zhai, Jinran Wu, Jian Yang, Dong Yue, Chunxia Dou, and Tengfei Zhang. A cloud endpoint coordinating captcha based on multi-view stacking ensemble. [7] Tariq, N., Asim, M., Khan, F. A., Baker, T., Khalid, U., & Derhab, A. (2021). A blockchain-based multi-mobile code-driven trust mechanism for detecting internal attacks in the Internet of Things. Sensors. [8] Derhab, A., Alawwad, R., Dehwah, K., Tariq, N., Khan, F. A., & Al-Muhtadi, J. (2021). Tweet-based bot detection using big data analytics. IEEE Access. [9] Tanthavech, N., & Nimkoompai, A. (2019). CAPTCHA: Impact of website security on user experience. Proceedings of the 4th International Conference on Intelligent Information Technology. [10] Chow, Y.-W., Susilo, W., & Thorncharoensri, P. (2019). CAPTCHA design and security issues. Advances in Cyber Security: Principles, Techniques, and Applications. [11] Hassan, T. U., Asim, M., Baker, T., Hassan, J., & Tariq, N. (2021). CTrust-RPL: A control layer-based trust mechanism for supporting secure routing in IoT applications. Transactions on Emerging Telecommunications Technologies. [12] Ouyang, Z., Zhai, X., Wu, J., Yang, J., Yue, D., Dou, C., & Zhang, T. (2021). A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble. IEEE Transactions on Information Forensics and Security. [13] IEEE Xplore Article (2025). Vulnerability analysis for CAPTCHAs using deep learning. IEEE Conference Publication. [14] Available at: https://ieeexplore.ieee.org?60?. [15] IEEE Xplore Article (2025). Using deep learning to solve Google reCAPTCHA v2’s image challenges. IEEE Conference Publication. Available at: https://ieeexplore.ieee.org?61?. [16] IEEE Xplore Article (2024). Image CAPTCHAs: When deep learning breaks the mold. IEEE Journals & Magazine. Available at: https://ieeexplore.ieee.org?62?. [17] Goodfellow, I., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR). [18] Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2017). The limitations of deep learning in adversarial settings. IEEE European Symposium on Security and Privacy. [19] Sivakorn, S., Polakis, I., & Keromytis, A. D. (2016). I am robot: (deep) learning to break semantic image CAPTCHAs. IEEE European Symposium on Security and Privacy. [20] Ye, G., Chen, X., & He, L. (2018). Yet another text captcha solver: A generative adversarial network based approach. IEEE Access. [21] Gao, H., Wang, H., Qi, J., Wang, X., Liu, X., & Deng, S. (2014). The robustness of text-based CAPTCHAs. IEEE Transactions on Information Forensics and Security. [22] Li, X., Yu, T., & Yan, J. (2020). Rethinking the security of CAPTCHAs in the age of deep learning. IEEE Transactions on Dependable and Secure Computing. [23] Bursztein, E., Martin, M., & Mitchell, J. (2011). Text-based CAPTCHA strengths and weaknesses. Proceedings of the 18th ACM Conference on Computer and Communications Security. [24] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial networks. Advances in Neural Information Processing Systems (NeurIPS). [25] Zhang, L., Li, W., & Liu, X. (2018). A survey on security issues of text-based CAPTCHAs. IEEE Communications Surveys & Tutorials. [26] Simard, P. Y., Steinkraus, D., & Platt, J. C. (2003). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR). [27] Wu, Y., He, J., & Liu, X. (2019). Deep learning-based CAPTCHA recognition and its security implications. IEEE Transactions on Cybernetics.

Copyright

Copyright © 2025 Geetha G, Kesavan M, Manoj Kumar M, Udhaya Kiran M R, Srinivasan R. 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.