Analysis of CAPTCHA Breakage- Employing Object Detection Deep Learning Methods to Identify CAPTCHA Characters - Dayanand; Jeberson, Wilson (International Journal of Computer Sciences and Engineering, 2018)
This research paper delves into the analysis of CAPTCHA breakage through the utilization of object detection deep learning techniques aimed at identifying CAPTCHA characters. CAPTCHAs, designed to differentiate between humans and bots, are widely used as a security measure on various online platforms. However, the effectiveness of traditional CAPTCHAs has been challenged by advancements in machine learning and artificial intelligence. This study explores the application of object detection methods within deep learning frameworks to bypass CAPTCHA security measures. Specifically, convolutional neural networks (CNNs) and other deep learning architectures are employed to detect and classify CAPTCHA characters, thus undermining their intended purpose. The research investigates the efficacy of these techniques in circumventing CAPTCHA challenges and analyzes the implications for online security. Through experimentation and evaluation, insights are gained into the vulnerabilities of current CAPTCHA systems and the potential threats posed by sophisticated machine learning algorithms. Additionally, considerations are made regarding the development of more robust CAPTCHA mechanisms to mitigate the risk of exploitation.