MEMECAHKAN CAPTCHA-TEXT TERDISTORSI DENGAN  CONVOLUTIONAL NEURAL NETWORK

Muhammad Akbar Yasin

doi:10.51401/jinteks.v6i1.3801

Authors

Muhammad Akbar Yasin Universitas Muhammadiyah Kendari

DOI:

https://doi.org/10.51401/jinteks.v6i1.3801

Keywords:

CAPTCHA, software security, CNN, deep learning, machine learning

Abstract

At the early stage of the development, CAPTCHA-text used distorted which difficult to solve by OCR technology. The development of AI technology, machine learning and image processing year after year makes the task to distinguish between human interaction and "bot" becomes more challenging. Recently, more advanced CAPTCHA types are available to avoid the risk of using CAPTCHA-text that can be considered irrelevant anymore to secure a website. However, until now can be found some websites that still use CAPTCHA-text. This paper contains the experimental results of developing an intelligent "bots" (using AI techniques) to solve the distorted text CAPTCHA. The Convolutional Neural Network was chosen as an approach for this study because its performance has proved excellent for object recognition applications. The CNN architecture used for this research consists of three convolutional layers, three pooling layers and two fully-connected layers. From the results of experiments conducted, the system managed to achieve a level of accuracy of 75% in ± 29 hours of program execution.

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