Analisis Robustness Teks Captcha Paypal HIP Menggunakan Template Matching

  • Fitrah Maharani Humaira Politeknik Negeri Madura
  • Tengku Musri Politeknik Negeri Bengkalis
  • Sarimuddin Sarimuddin Universitas Sembilan Belas November Kolaka
  • Dwi Samsuifin Alham Universitas Sembilan Belas November Kolaka
  • Aurista Miftahatul Ilmah Politeknik Negeri Madura
Keywords: CAPTCHA, CAPTCHA paypall, image processing, robustness, template matching


CAPTCHA refer to Completely Automated Public Turing test to tell Computers and Humans Apart. CAPTCHA are used to ensure that the operators are human not robots. The basic idea of using CAPTCHA is segmentation and recognition. Random characters, graphic images, or CAPTCHA audio become possible solutions to improve security and resilience for protection systems. In this paper used CAPTCHA random characters. However the CAPTCHA text needs to be analyzed again whether it is still solved by the computer or not it needs to be analyzed, improved, and developed to avoid automatic interference. Data set of text CAPTCHA paypal or so-called paypal HIP with 20 pieces of training data to get the template as much as 36 images that is from the numbers 0-9 and the letter A-Z. This particular paypal HIP data is limited by not using numbers 0 and 1 with the letters O and Q because of the similarity between the data. The method used starts from pre-processing, segmentation, and classification. Pre-processing techniques used consist of removing noise by tresholding and using cleaning techniques. We use bounding box and padding for segmentation method. And then for classification used counting pixel, vertical projections, horizontal projections, dan template correlation. By using these methods will be known which method can recognize CAPTCHA text accurately so as to affect the robustness of the CAPTCHA text.


Download data is not yet available.


Chen, C.J, You- Wei Wang, Wen-Pinn Fang, 2014, “A Study on CAPTCHA Recognition”, Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing;

Sakkatos, et. Al., 2014, “Analysis of Text-Based CAPTCHA Images using Template Matching Correlation Technique”, The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE-2014)

Kluever, K.A, 2008, “Breaking the Paypal HIP: A Comparison of Classifiers”

Tang, M. et. Al., 2016, “Research on Deep Learning Techniques in Breaking Text-based CAPTCHAs and Designing Image-based CAPTCHA”, IEEE Transactions On Information Forensics And Security, Vol. 14, No. 8

Banday, M.T, and Shafiya A. S, 2014, “Service Framework for Dynamic Multilingual CAPTCHA Challenges: IN-CAPTCHA”, International Conference on Advances in Electronics, Computers and Communications (ICAECC)

How to Cite
F. Humaira, T. Musri, S. Sarimuddin, D. Alham, and A. Ilmah, “Analisis Robustness Teks Captcha Paypal HIP Menggunakan Template Matching”, JAIC, vol. 2, no. 2, pp. 29-33, Dec. 2018.