A Generating Distorted CAPTCHA Images Using a Machine Learning Algorithm

被引：0

作者：

Salman, Saba Abdulbaqi ^{[1
]}

Mohialden, Yasmin Makki ^{[2
]}

Hussien, Nadia Mahmood ^{[2
]}

机构：

[1] Department of Computer Science, College of Education, Al-Iraqia University, Baghdad

[2] Department of Computer Science, College of Science, Mustansiriyah University, Baghdad

来源：

Iraqi Journal for Computer Science and Mathematics | 2024年 / 5卷 / 03期

关键词：

CAPTCHA; Image distortion; Machine learning; Random Forest classifier; Recognition; Security;

D O I：

10.52866/ijcsm.2024.05.03.023

中图分类号：

学科分类号：

摘要：

CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) have become universal in web security systems to differentiate between automated bots and human users. This research presents a novel approach for generating and classifying distorted CAPTCHA images utilizing machine learning techniques. The process involves developing a random text and rendering it onto an image, introducing distortion for security. The proposed method involves developing CAPTCHA images by combining text rendering and controlled distortion techniques. These images are then utilized to train a random forest classifier for accurate recognition. A Random Forest classifier is employed to recognize the generated CAPTCHA images. Experimental results demonstrate the approach's efficacy in achieving high validation accuracy. The validation accuracy of the classifier demonstrates its effectiveness in deciphering distorted images. Thus addressing the challenge of creating CAPTCHAs that are both human-readable and resistant to automated recognition. © 2024 College of Education, Al-Iraqia University. All rights reserved.

引用

页码：399 / 403

页数：4

共 15 条

[1] Ahn L. V., Blum M., Hopper N. J., Langford J., CAPTCHA: Using challenging AI problems for security, Theory and Application of Cryptographic Techniques, pp. 294-311, (2003)
[2] Bursztein E., Moscicki A., Fabry C., Bethard S., Mitchell J. C., Dan J., Easy does it: More usable CAPTCHAs, Proc. ACM Conf. Comput. Commun. Secur, pp. 1507-1518, (2014)
[3] Mori G., Malik J., Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA, IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, 2, pp. II-II, (2003)
[4] Abdulbaqi S. S., Al-Janabi S., Sagheer A. M., Security Attacks on E-Voting Systems Using Blockchain, Iraqi J. Comput. Sci. Math, 4, 2, pp. 179-188, (2023)
[5] Chellapilla K., Simard P. Y., I use Machine Learning to Break Visual Human Interaction Proofs (HIPs), Proc. Int. Conf. Doc. Anal. Recognit, pp. 265-272, (2004)
[6] Gao H., Yan J., Fang C., Zhang Z., Li J., A Simple Generic Attack on Text CAPTCHAs, Netw. Distrib. Syst. Secur. Symp. (NDSS), (2016)
[7] George D., Et al., A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs, Science, 358, 6368, (2017)
[8] Kong S., Chang Y.-C., A CAPTCHA Recognition Algorithm Based on Character Segmentation and Random Forest, J. Phys.: Conf. Ser, 2504, 1, (2023)
[9] Thobhani A., Gao M., Hawbani A., Ali S. T. M., Abdussalam A., CAPTCHA Recognition Using Deep Learning with Attached Binary Images, Electronics, 9, 9, (2020)
[10] Alqahtani F. H., Alsulaiman F. A., Is image-based CAPTCHA secure against attacks based on machine learning? An experimental study, Comput. Secur, 88, (2020)

← 1 2 →