Visible to the public Challenge to Impede Deep Learning against CAPTCHA with Ergonomic Design

TitleChallenge to Impede Deep Learning against CAPTCHA with Ergonomic Design
Publication TypeConference Paper
Year of Publication2017
AuthorsAzakami, T., Shibata, C., Uda, R.
Conference Name2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)
Date Publishedjul
Keywordsamodal completion, CAPTCHA, CAPTCHA algorithm, captchas, character recognition, CNN, composability, Convolutional Net Work, Deep Learning, ergonomic design, ergonomics, Human Behavior, human beings, human factors, Image color analysis, image recognition, learning (artificial intelligence), machine learning, Neural networks, Optical character recognition software, pubcrawl, text analysis, unbreakable CAPTCHA
Abstract

Once we had tried to propose an unbreakable CAPTCHA and we reached a result that limitation of time is effect to prevent computers from recognizing characters accurately while computers can finally recognize all text-based CAPTCHA in unlimited time. One of the existing usual ways to prevent computers from recognizing characters is distortion, and adding noise is also effective for the prevention. However, these kinds of prevention also make recognition of characters by human beings difficult. As a solution of the problems, an effective text-based CAPTCHA algorithm with amodal completion was proposed by our team. Our CAPTCHA causes computers a large amount of calculation costs while amodal completion helps human beings to recognize characters momentarily. Our CAPTCHA has evolved with aftereffects and combinations of complementary colors. We evaluated our CAPTCHA with deep learning which is attracting the most attention since deep learning is faster and more accurate than existing methods for recognition with computers. In this paper, we add jagged lines to edges of characters since edges are one of the most important parts for recognition in deep learning. In this paper, we also evaluate that how much the jagged lines decrease recognition of human beings and how much they prevent computers from the recognition. We confirm the effects of our method to deep learning.

URLhttp://ieeexplore.ieee.org/document/8029670/
DOI10.1109/COMPSAC.2017.83
Citation Keyazakami_challenge_2017