ARTiFACIAL: automated reverse turing test using FACIAL features

ABSTRACT

Web services designed for human users are being abused by computer programs (bots). The bots steal thousands of free email accounts in a minute; participate in online polls to skew results; and irritate people by joining online chat rooms. These real-world issues have recently generated a new research area called Human Interactive Proofs (HIP), whose goal is to defend services from malicious attacks by differentiating bots from human users. In this paper, we propose a new HIP algorithm based on detecting human face and facial features. Human faces are the most familiar object to humans, rendering it possibly the best candidate for HIP. We conducted user studies and showed the ease of use of our system to human users. We designed attacks using the best existing face detectors and demonstrated the difficulty to bots.

REFERENCES

• 1: Ahn, L., Blum, M., and Hopper, N. J., Telling humans and computers apart (Automatically) or How lazy cryptographers do AI, Technical Report CMU-CS-02-117, February, 2002
• 2: AltaVista's Add URL site: altavista.com/sites/addurl/newurl
• 3: Henry S. Baird , Kris Popat, Human Interactive Proofs and Document Image Analysis, Proceedings of the 5th International Workshop on Document Analysis Systems V, p.507-518, August 19-21, 2002
• 4: A. Colmenarez , T. Huang, Face Detection With Information-Based Maximum Discrimination, Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), p.782, June 17-19, 1997
• 5: Naor, M., Verification of a human in the loop or identification via the Turing test, unpublished notes, September 13, 1996
• 6: Rui, Y. and Liu, Z., ARTiFACIAL: Automated Reverse Turing test using FACIAL features, MSR TR 2003-48
• 7: Turing, A., Computing machinery and intelligence, Mind, Vol. 59 (236), pp. 433--460, 1950
• 8: Shuicheng Yan , Mingjing Li , Hongjiang Zhang , Qiansheng Cheng, Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework, Proceedings of the Ninth IEEE International Conference on Computer Vision, p.51, October 13-16, 2003
• 9: Ming-Hsuan Yang , David J. Kriegman , Narendra Ahuja, Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.24 n.1, p.34-58, January 2002 [doi>10.1109/34.982883]
• 10: Yang, M., Roth, D., and Ahuja, N., A SNoW-Based Face Detector, Advances in Neural Information Processing Systems 12 (NIPS 12), S.A. Solla, T.K. Leen and K.-R. Muller (eds), pp. 855--861, MIT Press, 2000.
• 11: ZhenQiu Zhang1 , Long Zhu , Stan Z. Li , HongJiang Zhang, Real-Time Multi-View Face Detection, Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, p.149, May 20-21, 2002

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Computer vision

Additional Classification:
  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.3 Picture/Image Generation
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.9 Applications
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Signal processing

General Terms:
Algorithms, Design, Human Factors, Security, Verification

Keywords:
CAPTCHA, face and facial feature detection, human interactive proof (HIP), turing test, web services security