Designing Human Friendly Human Interaction Proofs (HIPs)

1. Designing Human FriendlyHuman Interaction Proofs (HIPs) Kumar Chellapilla, Kevin Larson, Patrice Simard and Mary Czerwinski Microsoft Research Presented by Shaohua Xie March 22, 2005

2. OUTLINE • Introduction • Definitions • User Study I • User Study II • Conclusion • References

3. Introduction HIPs, or Human Interactive Proofs, are challenges meant to be easily solved by humans, while remaining too hard to be economically solved by computers. An example character based HIP

4. Introduction HIPs are increasingly used to protect services against automatic script attacks. Mailblocks HIP samples.

5. Introduction MSN HIP samples. Register.com HIP samples.

6. Introduction EZ-Gimpy HIP samples.

7. Introduction YAHOO! HIP samples. Ticketmaster HIP samples.

8. Introduction Google HIP samples.

9. OUTLINE • Introduction • Definitions • User Study I • User Study II • Conclusion • References

10. Definitions • Plain text => Global Warp => • Plain text => Local Warp =>

11. Definitions Level 10 • Translated Text Level 25 Level 40 Level 15 Level 30 • Rotated Text Level 45

12. Definitions • Scaled Text Level 20 Level 35 Level 50

13. OUTLINE • Introduction • Definitions • User Study I • User Study II • Conclusion • References

14. User Study I • HIPs that only varied on one parameter of distortion are presented to users. • Accuracy: the percentage of characters correctly recognized. • For the parameter levels testedon plain, translated, rotated or scaled text HIPs, userswere at 99% correct or higher.

15. User Study I • Global Warp Text Level 180 Level 270 Level 360

16. User Study I • Local Warp Text Level 30 Level 55 Level 80

17. OUTLINE • Introduction • Definitions • User Study I • User Study II • Conclusion • References

18. User Study II • Unidimensional HIPs has been systematically broken, with a success rate of 5% or greater at a rate of 300 attempts per second [2,12]. • Arcs and baselines are added to make HIPs very hard for computers to break.

19. User Study II • Thin Arcs that intersect plus baseline #Arcs:0 #Arcs:18 #Arcs:36

20. User Study II • Thick Arcs that intersect plus baseline

21. User Study II • Thick Arcs that don’t intersect plus baseline #Arcs:0 #Arcs:18 #Arcs:36

22. OUTLINE • Introduction • Definitions • User Study I • User Study II • Conclusion • References

23. Conclusion • Most one-dimensional HIPs are easy for users to solve. • However, there is a significant decrease inhuman HIP solution accuracy with the increase of the global or local warping levels. • Accuracy was also quite high across alllevels of HIP recognition with thin arcs in the foreground. • Adding intersecting thick arcs caused significant performancedecrements, but non-intersecting thick arcs did not.

24. OUTLINE • Introduction • Definitions • User Study I • User Study II • Conclusion • References

25. References • Simard PY, Szeliski R, Benaloh J, Couvreur J, andCalinov I (2003), “Using Character Recognition andSegmentation to Tell Computers from Humans,”International Conference on Document Analysis andRecognition (ICDAR), IEEE Computer Society, pp.418-423, 2003. • Chellapilla K., and Simard P., “Using MachineLearning to Break Visual Human Interaction Proofs(HIPs),” Advances in Neural Information ProcessingSystems 17, Neural Information Processing Systems(NIPS’2004), MIT Press. • Turing AM (1950), “Computing Machinery andIntelligence,” Mind, vol. 59, no. 236, pp. 433-460. • Von Ahn L, Blum M, and Langford J. (2004) “TellingComputers and Humans Apart (Automatically) orHow Lazy Cryptographers do AI.” Comm. of the ACM,47(2):56-60. • First Workshop on Human Interactive Proofs, PaloAlto, CA, January 2002. • Von Ahn L, Blum M, and Langford J, The CaptchaProject. http://www.captcha.net

26. References • Mori G, Malik J (2003), “Recognizing Objects inAdversarial Clutter: Breaking a Visual CAPTCHA,”Proceedings of the Computer Vision and PatternRecognition (CVPR) Conference, IEEE ComputerSociety, vol.1, pages:I-134 - I-141, June 18-20, 2003 • Chew, M. and Baird, H. S. (2003), “BaffleText: aHuman Interactive Proof,” Proc., 10th IS&T/SPIEDocument Recognition & Retrieval Conf., Santa Clara,CA, Jan. 22. • Simard, P.,Y., Steinkraus, D., Platt, J. (2003) “BestPractice for Convolutional Neural Networks Appliedto Visual Document Analysis,” InternationalConference on Document Analysis and Recognition(ICDAR), IEEE Computer Society, Los Alamitos, pp.958-962, 2003. • Selfridge, O.G. (1959). Pandemonium: A paradigm forlearning. In Symposium in the mechanization ofthought process (pp.513-526). London: HM StationeryOffice. • Pelli, D. G., Burns, C. W., Farrell, B., & Moore, D. C,“Identifying letters.” (accepted) Vision Research. • Goodman J. and Rounthwaite R., “Stopping OutgoingSpam,” Proc. of the 5th ACM conf. on Electroniccommerce, New York, NY. 2004.