Robust CAPTCHA Image Generation Enhanced with Adversarial Example Methods-Reference-Cited by-同舟云学术

Robust CAPTCHA Image Generation Enhanced with Adversarial Example Methods

Published:2020-04-01 Issue:4 Volume:E103.D Page:879-882
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

KWON Hyun¹²,YOON Hyunsoo¹,PARK Ki-Woong³

Affiliation:

1. School of Computing, Korea Advanced Institute of Science and Technology

2. Department of Electrical Engineering, Korea Military Academy

3. Department of Computer and Information Security, Sejong University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E103.D/4/E103.D_2019EDL8194/_pdf

Reference11 articles.

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2. [2] E. Bursztein, M. Martin, and J. Mitchell, “Text-based captcha strengths and weaknesses,” Proceedings of the 18th ACM conference on Computer and communications security, pp.125-138, ACM, 2011. 10.1145/2046707.2046724

3. [3] A. Hindle, M.W. Godfrey, and R.C. Holt, “Reverse engineering captchas,” 2008 15th Working Conference on Reverse Engineering, pp.59-68, IEEE, 2008. 10.1109/wcre.2008.35

4. [4] R. Hussain, K. Kumar, H. Gao, and I. Khan, “Recognition of merged characters in text based captchas,” 2016 IEEE 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp.3917-3921, 2016.

5. [5] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199, 2013.

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