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Game-based image semantic CAPTCHA on handset devices

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Abstract

A completely automated public turing test to tell computer and human apart (CAPTCHA) is based on the Turing test, which aims to protect Internet services from automatic script attacks and spams. However, most proposed or deployed CAPTCHAs have been breached. It is possible to enhance the security of an existing CAPTCHA by adding noises systematically adding noises, but distortions would make characters recognition difficult for humans. On the other hand, most of the traditional CPATCHAs require complicated operations using keyboards and mice which may become limitations of modern handset devices. In this study, we propose a novel GISCHA using game-based image semantics with the contributions that 1) use simple keys, mouse, gesture, and accelerometer instead of complex alphabet inputs; 2) is language independent; 3) enhances the security level without annoying users; 4) is based on more advanced human cognitive abilities; and 5) make CAPTCHAs more interesting. The experiment results show that a single GISCHA challenge was completed in 9.06 s on average with a virtual keyboard and 10.25 s on average with accelerometers build in handset devices, and the pass rate of first time use is 94.8 %, which means that it is sufficiently easy for practical use.

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References

  1. Bigham JP, Cavender AC (2009) Evaluating existing audio CAPTCHAs and an interface optimized for non-visual use. In: CHI ‘09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp 1829–1838

  2. Birgale L, Kokare M (2012) Iris recognition using ridgelets. J Inf Process Syst 8:445–458. doi:10.3745/JIPS.2012.8.3.445

    Article  Google Scholar 

  3. Bin B Zhu, Yan J, Li Q, et al. (2010) Attacks and design of image recognition CAPTCHAs. In: CCS ‘10: Proceedings of the 17th ACM conference on Computer and communications security. pp 187–200

  4. Bursztein E, Bethard S, Fabry C, et al. (2010) How good are humans at solving CAPTCHAs? a large scale evaluation. In: Security and Privacy (SP), 2010 I.E. Symposium on. IEEE, pp 399–413

  5. Bursztein E, Martin M, Mitchell J (2011) Text-based CAPTCHA strengths and weaknesses. In: CCS ‘11: Proceedings of the 18th ACM conference on Computer and communications security. ACM, pp 125–138

  6. Chandavale AA, Sapkal A (2012) A New Approach towards Segmentation for Breaking CAPTCHA. In: International Conference on Security in Computer Networks and Distributed Systems. Springer, pp 323–335

  7. Chellapilla K, Larson K, Simard P, Czerwinski M (2005) Computers beat humans at single character recognition in reading based human interaction proofs (HIPs). CEAS 2005—Second Conference on Email and Anti-Spam

  8. Chew M, Tygar JD (2004) Image recognition captchas. In: Information Security, 7th International Conference. Springer, pp 268–279

  9. Chow R, Golle P, Jakobsson M, et al. (2008) Making CAPTCHAs clickable. In: HotMobile ‘08 Proceedings of the 9th workshop on Mobile computing systems and applications. pp 91–94

  10. Elson J, Douceur JR, Howell J, Saul J (2007) Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In: ACM Conference on Computer and Communications Security. pp 366–374

  11. Fang K, Bu Z, Xia ZY (2012) Segmentation of CAPTCHAs based on complex networks. In: 2012 AICI Annual Conference. Springer, pp 735–743

  12. Golle P (2008) Machine learning attacks against the Asirra CAPTCHA. In: CCS ‘08: Proceedings of the 15th ACM conference on Computer and communications security. ACM, pp 535–542

  13. Huang S-Y, Lee Y-K, Bell G, Ou Z-H (2010) An efficient segmentation algorithm for CAPTCHAs with line cluttering and character warping. Multimedia Tool Appl 48:267–289. doi:10.1007/s11042-009-0341-5

    Article  Google Scholar 

  14. Kim J-J, Hong S-P (2011) A method of risk assessment for multi-factor authentication. J Inf Process Syst 7:187–198. doi:10.3745/JIPS.2011.7.1.187

    Article  Google Scholar 

  15. Kim J, Kim S, Yang J et al (2013) FaceCAPTCHA: a CAPTCHA that identifies the gender of face images unrecognized by existing gender classifiers. Multimedia Tool Appl. doi:10.1007/s11042-013-1422-z

    Google Scholar 

  16. Li S, Shah SAH, Khan MAU, et al. (2010) Breaking e-banking CAPTCHAs. In: ACSAC ‘10: Proceedings of the 26th Annual Computer Security Applications Conference. pp 171–180

  17. Manuel B, Louis von A, John L, Nick H (2000) The CAPTCHA Project. In: The CAPTCHA. http://www.captcha.net/. Accessed 11 Feb 2013

  18. Mori G, Malik J (2003) Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. In: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 I.E. Computer Society Conference on. IEEE, pp 134–141

  19. Satone MP, Kharate GK (2012) Face Recognition Based on PCA on Wavelet Subband of Average-Half-Face. J Inf Process Syst 8:483–494. doi:10.3745/JIPS.2012.8.3.483

    Article  Google Scholar 

  20. Simard PY (2004) Using machine learning to break visual human interaction proofs. Adv Neural Inf Process Syst 17:265–272

    Google Scholar 

  21. Turing A (1950) Computing Machinery and Intelligence. Mind 49:433–460

    Article  MathSciNet  Google Scholar 

  22. von Ahn L (2009) Human computation. In: Design Automation Conference, 2009. DAC ‘09. 46th ACM/IEEE. pp 418–419

  23. von Ahn L, Blum M, Hopper N, Langford J (2003) CAPTCHA: using hard AI problems for security. Adv Cryptol

  24. von Ahn L, Blum M, Langford J (2004) Telling humans and computers apart automatically. Commun ACM 47:56–60

    Article  Google Scholar 

  25. von Ahn L, Maurer B, McMillen C et al (2008) recaptcha: Human-based character recognition via web security measures. Science 321:1465–1468

    Article  MATH  MathSciNet  Google Scholar 

  26. Yamamoto T, Suzuki T, Nishigaki M (2010) A Proposal of Four-Panel Cartoon CAPTCHA: The Concept. In: Network-Based Information Systems (NBiS), 2010 13th International Conference on. pp 575–578

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Acknowledgments

The authors would like to thank the National Taichung University of Education and Ministry of Education, Taiwan, for financially supporting this research under grants of the talent nurturing pioneer program for proactive SoC design - embedded systems and software engineering.

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Authors and Affiliations

  1. Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan, 300

    Tzu-I Yang & Chien-Chao Tseng

  2. Department of Computer Science, National Taichung University of Education, 140 Minsheng Road, Taichung City, Taiwan, 403

    Chorng-Shiuh Koong

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  1. Tzu-I Yang
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  3. Chien-Chao Tseng
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Correspondence to Tzu-I Yang.

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Yang, TI., Koong, CS. & Tseng, CC. Game-based image semantic CAPTCHA on handset devices. Multimed Tools Appl 74, 5141–5156 (2015). https://doi.org/10.1007/s11042-013-1666-7

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