Unveiling CAPTCHA Vulnerabilities: Breaking CAPTCHA Using Deep Learning Techniques and Design and Development of Robust CAPTCHA Technique
Day anand, Wilson Jeberson, Klinsega Jeberson · International Journal of Electrical and Electronics Engineering · 2024
CAPTCHA serves as a vital tool in distinguishing between human users and automated bots attempting to access websites. The Turing test, a fundamental concept in this domain, aids in discerning robot involvement in web security breaches, thereby safeguarding against automated access and potential harm. CAPTCHA, encapsulated by the acronym Completely Automated Public Turing Test to Tell Computers and Humans Apart, is instrumental in preventing undesirable activities by posing tasks that humans find simple to solve yet prove exceedingly challenging for robots, representing a distinct category of challenges. Initiatives aimed at improving CAPTCHA systems have led to the development of models aimed at accurately recognizing characters within CAPTCHA images. While traditional methods require the segmentation of characters before recognition, proposed models eliminate this step by processing the entire image at once, resulting in improved accuracy. Convolutional Neural Networks (CNNs) exhibit enhanced accuracy with segmented characters, while Multi-Task Convolutional Neural Network (MTCNN) excels in achieving similar accuracy without pre-processing. Object detection algorithms, including Faster R-CNN, YOLO, and SSD, offer even greater potential for breaking CAPTCHA by detecting objects within images. Gesture-based CAPTCHA challenges, while promising, encounter usability issues related to precision, reaction speed, and perceived level of challenge. To address this, a novel approach is proposed, leveraging hand-based gestures that are easily solvable by humans yet challenging for robots to replicate. Additionally, dynamic game-based CAPTCHA designs offer an aesthetically appealing and engaging interface, potentially motivating users to solve CAPTCHA challenges with minimal annoyance. The objective of this study is to explore the influence of different CAPTCHA tests on user experience across diverse populations. It includes a comprehensive study of multitask learning convolutional neural networks and employs methods of object detection algorithms, including Faster R-CNN, YOLO, and SSD object detection for CAPTCHA character recognition. The research also encompasses the design of gesture-based and dynamic game-based CAPTCHA challenges and compares various deep learning CAPTCHA breaking techniques with SSD object detection methods, analyzing existing and designed CAPTCHAs on multiple parameters.