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Robust CAPTCHA Image Generation Enhanced with Adversarial Example Methods

Hyun KWON, Hyunsoo YOON, Ki-Woong PARK

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Summary :

Malicious attackers on the Internet use automated attack programs to disrupt the use of services via mass spamming, unnecessary bulletin boarding, and account creation. Completely automated public turing test to tell computers and humans apart (CAPTCHA) is used as a security solution to prevent such automated attacks. CAPTCHA is a system that determines whether the user is a machine or a person by providing distorted letters, voices, and images that only humans can understand. However, new attack techniques such as optical character recognition (OCR) and deep neural networks (DNN) have been used to bypass CAPTCHA. In this paper, we propose a method to generate CAPTCHA images by using the fast-gradient sign method (FGSM), iterative FGSM (I-FGSM), and the DeepFool method. We used the CAPTCHA image provided by python as the dataset and Tensorflow as the machine learning library. The experimental results show that the CAPTCHA image generated via FGSM, I-FGSM, and DeepFool methods exhibits a 0% recognition rate with ε=0.15 for FGSM, a 0% recognition rate with α=0.1 with 50 iterations for I-FGSM, and a 45% recognition rate with 150 iterations for the DeepFool method.

Publication
IEICE TRANSACTIONS on Information Vol.E103-D No.4 pp.879-882
Publication Date
2020/04/01
Publicized
2020/01/15
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDL8194
Type of Manuscript
LETTER
Category
Information Network

Authors

Hyun KWON
  Korea Advanced Institute of Science and Technology,Korea Military Academy
Hyunsoo YOON
  Korea Advanced Institute of Science and Technology
Ki-Woong PARK
  Sejong University

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