A Comparative Analysis of CNN, RCNN & Faster RCNN Object Detection Algorithm for CAPTCHA Breaking
Keywords:
CAPTCHA, Object Detection, Faster R-CNN, CNN, Machine Learning, Deep LearningAbstract
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems serve as a crucial defense mechanism against automated attacks by distinguishing between human users and bots. However, advancements in deep learning have posed significant challenges to the security of conventional CAPTCHA systems. In this research paper, we present a comparative analysis of two prominent object detection algorithms, Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (RCNN), for breaking CAPTCHAs. The study evaluates the performance of CNN and RCNN algorithms in accurately identifying and deciphering characters within CAPTCHA images. Utilizing a diverse dataset of CAPTCHA samples, experiments are conducted to assess the effectiveness of both algorithms in handling variations in CAPTCHA styles, languages, and complexities. Through extensive experimentation and evaluation, we analyze the strengths and limitations of CNN and RCNN in the context of CAPTCHA breaking. Key metrics such as accuracy, precision, recall, and computational efficiency are compared to provide insights into the relative performance of each algorithm. The findings of this research contribute to the understanding of object detection techniques for CAPTCHA breaking and provide valuable insights for enhancing the security of CAPTCHA systems against emerging threats posed by deep learning-based attacks.
References
A. Broder, "Method for selectively restricting access to computer systems," U.S. Patent 6,195,698, 2001.
S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587.
S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," in Advances in Neural Information Processing Systems, 2015, pp. 91-99.
C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.
Y. Zi, H. Gao, Z. Cheng, and Y. Liu, "An end-to-end attack on text CAPTCHAs," IEEE Transactions, 2018.
S. Sachdev, "Breaking CAPTCHA characters using Multi-Task Learning CNN and SVM," IEEE Digital Library, 2020.
Y. Soullard, C. Ruffino, and T. Paquet, "CTCModel: a Keras Model for Connectionist Temporal Classification," LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes, 2019.
Y. Shu and Y. Xu, "End-to-End Captcha Recognition Using Deep CNN-RNN Network," IEEE, 2019.
E. Ababtain and D. Engels, "Gestures Based CAPTCHAs: The Use of Sensor Readings to Solve CAPTCHA Challenge on Smartphones," IEEE, 2019.
Y. LeCun et al., "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, 1998.
S. Ren et al., "Faster R-CNN: Towards real-time object detection with region proposal networks," in Advances in Neural Information Processing Systems, 2015.
H. Wang, F. Zheng, Z. Chen, Y. Lu, J. Gao, and R. Wei, "A CAPTCHA Design Based on Visual Reasoning," IEEE, 2018.
P. Panwar, P. Monika, P. Kumar, and A. Sharma, "CHGR: Captcha generation using Hand Gesture Recognition," IEEE, 2018.
M. Mohamed, S. Gao, N. Sachdeva, N. Saxena, and C. Zhang, "On the Security and Usability of Dynamic Cognitive Game CAPTCHAs," IOS Press, 2017.
