A Generating Distorted CAPTCHA Images Using a Machine Learning Algorithm

Abstract

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.

Keywords

• CAPTCHA, Random Forest classifier, Security, Machine learning, Recognition, Image distortion