A security analysis of automated Chinese turing tests

Abdalnaser Algwil, Dan Ciresan, Beibei Liu, Jeff Yan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

18 Citations (Scopus)

Abstract

Text-based Captchas have been widely used to deter misuse of services on the Internet. However, many designs have been broken. It is intellectually interesting and practically relevant to look for alternative designs, which are currently a topic of active research. We motivate the study of Chinese Captchas as an interesting alternative design - counterintuitively, it is possible to design Chinese Captchas that are universally usable, even to those who have never studied Chinese language. More importantly, we ask a fundamental question: is the segmentation-resistance principle established for Roman-character based Captchas applicable to Chinese based designs? With deep learning techniques, we offer the first evidence that computers do recognize individual Chinese characters well, regardless of distortion levels. This suggests that many real-world Chinese schemes are insecure, in contrast to common beliefs. Our result offers an essential guideline to the design of secure Chinese Captchas, and it is also applicable to Captchas using other large alphabet languages such as Japanese.

Original languageEnglish
Title of host publicationProceedings - 32nd Annual Computer Security Applications Conference, ACSAC 2016
Pages520-532
Number of pages13
ISBN (Electronic)9781450347716
DOIs
Publication statusPublished - 5 Dec 2016
Event32nd Annual Computer Security Applications Conference, ACSAC 2016 - Los Angeles, United States
Duration: 5 Dec 20169 Dec 2016

Publication series

NameACM International Conference Proceeding Series
Volume5-9-December-2016

Conference

Conference32nd Annual Computer Security Applications Conference, ACSAC 2016
Country/TerritoryUnited States
CityLos Angeles
Period5/12/169/12/16

Keywords

  • Chinese captcha
  • convolutional neural network
  • deep neural network
  • security
  • usability

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