Unveiling CAPTCHA Vulnerabilities: Breaking CAPTCHA Using Deep Learning Techniques and Design and Development of Robust CAPTCHA Technique
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 6 |
| Year of Publication : 2024 |
| Authors : Dayanand, Wilson Jeberson, Klinsega Jeberson |
10.14445/23488379/IJEEE-V11I6P130
How to Cite?
Dayanand, Wilson Jeberson, Klinsega Jeberson, "Unveiling CAPTCHA Vulnerabilities: Breaking CAPTCHA Using Deep Learning Techniques and Design and Development of Robust CAPTCHA Technique," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 6, pp. 282-304, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P130
Abstract:
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.
Keywords:
CAPTCHA, Gesture-based CAPTCHA, Dynamic game based CAPTCHA, RNN, Faster RCNN, SSD.
References:
[1] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Jeff Heaton, “Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning,” Genetic Programming and Evolvable Machines, vol. 19, pp. 305-307, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shaoqing Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, pp. 779-788, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Wei Liu et al., “SSD: Single Shot MultiBox Detector,” Computer Vision – ECCV 2016, pp. 21-37, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Lazzat Zulpukharkyzy Zholshiyeva et al., “Hand Gesture Recognition Methods and Applications: A Literature Survey,” ICEMIS'21: The 7th International Conference on Engineering & MIS, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] S. Ashok Kumar et al., “Gamification of Internet Security by Next Generation CAPTCHAs,” 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1-5, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Yang Zi et al., “An End-to-End Attack on Text CAPTCHAs” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 753-766, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Sumeet Sachdev, “Breaking CAPTCHA Characters Using Multi-Task Learning CNN and SVM,” 2020 4th International Conference on Computational Intelligence and Networks (CINE), Kolkata, India, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Yann Soullard, Cyprien Ruffino, and Thierry Paquet, “CTCModel: A Keras Model for Connectionist Temporal Classification,” arXiv, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yujin Shu, and Yongjin Xu, “End-to-End Captcha Recognition Using Deep CNN-RNN Network,” 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC 2019), Chongqing, China, pp. 54-58, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Eman Ababtain, and Daniel Engels, “Gestures Based CAPTCHAs the Use of Sensor Readings to Solve CAPTCHA Challenge on Smartphones,” 2019 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, USA, pp. 113-119, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Song Gao et al., “Emerging-Image Motion CAPTCHAs: Vulnerabilities of Existing Designs, and Countermeasures,” IEEE Transactions on Dependable and Secure Computing, vol. 16, no. 6, pp. 1040-1053, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Pooja Panwar et al., “CHGR: Captcha Generation Using Hand Gesture Recognition,” 2018 Conference on Information and Communication Technology (CICT’18), Jabalpur, India, pp. 1-6, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Manar Mohamed et al., “On the Security and Usability of Dynamic Cognitive Game CAPTCHAs,” Journal of Computer Security, vol. 25, no. 3, pp. 205-230, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Jiayi Fan et al., “Improvement of Object Detection Based on Faster R-CNN and YOLO,” 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Jeju, Korea (South), pp. 1-4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Qianjun Shuai, and Xingwen Wu, “Object Detection System Based on SSD Algorithm,” 2020 International Conference on CultureOriented Science & Technology (ICCST), Beijing, China, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Upulie Handalage, and Lakshini Kuganandamurthy, “Real-Time Object Detection Using YOLO: A Review,” pp. 1-6, 2021.
[Google Scholar]
[19] Haipeng Wang et al., “A Captcha Design Based on Visual Reasoning,” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, pp. 1967-1971, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Puneet, and Deepika, “Redefining Security: Unveiling the Vulnerabilities of Captcha Mechanisms Using Deep Learning,” 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Timofey A. Korzhebin, and Alexey D. Egorov, “Comparison of Combinations of Data Augmentation Methods and Transfer Learning Strategies in Image Classification Used in Convolution Deep Neural Networks,” 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), Moscow, Russia, pp. 479-482, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ravpreet Kaur, and Sarbjeet Singh, “A comprehensive Review of Object Detection with Deep Learning,” Digital Signal Processing, vol. 132,2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Cheng Wang, and Zhihao Peng, “Design and Implementation of an Object Detection System Using Faster R-CNN,” 2019 International Conference on Robots & Intelligent System (ICRIS), Haikou, China, pp. 204-206, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Feng-Lin Du et al., “CAPTCHA Recognition Based on Faster R-CNN,” Intelligent Computing Theories and Application, pp. 597-605, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] G. Mori, and J. Malik, “Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA,” 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, USA, pp. 1-1, 2003.
[CrossRef] [Google Scholar] [Publisher Link]