skip to main content
10.1145/1455770.1455838acmconferencesArticle/Chapter ViewAbstractPublication PagesccsConference Proceedingsconference-collections
research-article

Machine learning attacks against the Asirra CAPTCHA

Published: 27 October 2008 Publication History

Abstract

The Asirra CAPTCHA [EDHS2007], proposed at ACM CCS 2007, relies on the problem of distinguishing images of cats and dogs (a task that humans are very good at). The security of Asirra is based on the presumed difficulty of classifying these images automatically.
In this paper, we describe a classifier which is 82.7% accurate in telling apart the images of cats and dogs used in Asirra. This classifier is a combination of support-vector machine classifiers trained on color and texture features extracted from images. Our classifier allows us to solve a 12-image Asirra challenge automatically with probability 10.3%. This probability of success is significantly higher than the estimate of 0.2% given in [EDHS2007] for machine vision attacks. Our results suggest caution against deploying Asirra without safeguards.
We also investigate the impact of our attacks on the partial credit and token bucket algorithms proposed in [EDHS2007]. The partial credit algorithm weakens Asirra considerably and we recommend against its use. The token bucket algorithm helps mitigate the impact of our attacks and allows Asirra to be deployed in a way that maintains an appealing balance between usability and security. One contribution of our work is to inform the choice of safeguard parameters in Asirra deployments.

References

[1]
ASR Asirra: A Human Interactive Proof. On the Web at http://research.microsoft.com/asirra/
[2]
BotBarrier.com. On the web at http://www.botbarrier.com/
[3]
Chih-Chung Chang and Chih-Jen Lin. LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
[4]
. Chow, P. Golle, M. Jakobsson, X. Wang and L. Wang. Making CAPTCHAs Clickable. In Proc. of HotMobile 2008.
[5]
. Cortes and V. Vapnik. Support-vector network. Machine Learning 20, 273--297, 1995.
[6]
. Douceur and J. Elson. Private communication.
[7]
. Elson, J. Douceur, J. Howell and J. Saul. Asirra: a CAPTCHA that exploits interest-aligned manual image categorization. In Proc. of ACM CCS 2007, pp. 366--374.
[8]
. Golle and D. Wagner. Cryptanalysis of a Cognitive Authentication Scheme. In Proc. of the 2007 IEEE Symposium on Security and Privacy, pp.66--70. IEEE Computer Society
[9]
Google CAPTCHA. On the web at https://www.google.com/accounts/DisplayUnlockCaptcha
[10]
. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning (Data Mining, Inference, and Prediction). Springer Series in Statistics, 2001.
[11]
. Kruizinga, N. Petkov and S.E. Grigorescu. Comparison of texture features based on Gabor filters. In Proc. of the 10th International Conference on Image Analysis and Processing (1999), pp. 142--147.
[12]
. Lopresti. Leveraging the CAPTCHA problem. In Proc. of the Second International Workshop on Human Interactive Proofs, pp. 97--110. Springer Verlag, 2005.
[13]
. Mironov and L. Zhang. Applications of SAT Solvers to Cryptanalysis of Hash Functions. In Theory and Applications of Satisfiability Testing -- SAT 2006, pp. 102--115, 2006.
[14]
. Mori and J. Malik. Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. In Proc. of the 2003 Conference on Computer Vision and Pattern Recognition, pp. 134--144. IEEE Computer Society, 2003.
[15]
SlashDot. Yahoo CAPTCHA Hacked (posted Jan 29, 2008). On the Web at http://it.slashdot.org/it/08/01/30/0037254.shtml
[16]
Websense Blog (posted Feb 22, 2008). Google's CAPTCHA busted in recent spammer tactics. On the web at http://securitylabs.websense.com/content/Blogs/2919.aspx
[17]
. Yan and A. El Ahmad. A Low-cost Attack on a Microsoft CAPTCHA. To appear in Proc. of ACM CCS 2008.

Cited By

View all
  • (2024)Man and the Machine: Effects of AI-assisted Human Labeling on Interactive Annotation of Real-time Video StreamsACM Transactions on Interactive Intelligent Systems10.1145/364945714:2(1-22)Online publication date: 29-Feb-2024
  • (2024) ImageVeriBypasser : An image verification code recognition approach based on Convolutional Neural Network Expert Systems10.1111/exsy.13658Online publication date: 25-Jun-2024
  • (2024)Image CAPTCHAs: When Deep Learning Breaks the MoldIEEE Access10.1109/ACCESS.2024.344297612(112211-112231)Online publication date: 2024
  • Show More Cited By

Index Terms

  1. Machine learning attacks against the Asirra CAPTCHA

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      CCS '08: Proceedings of the 15th ACM conference on Computer and communications security
      October 2008
      590 pages
      ISBN:9781595938107
      DOI:10.1145/1455770
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 27 October 2008

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. captcha
      2. classifier
      3. machine learning
      4. reverse turing test
      5. support vector machine

      Qualifiers

      • Research-article

      Conference

      CCS08
      Sponsor:

      Acceptance Rates

      CCS '08 Paper Acceptance Rate 51 of 280 submissions, 18%;
      Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

      Upcoming Conference

      CCS '25

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)45
      • Downloads (Last 6 weeks)6
      Reflects downloads up to 07 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Man and the Machine: Effects of AI-assisted Human Labeling on Interactive Annotation of Real-time Video StreamsACM Transactions on Interactive Intelligent Systems10.1145/364945714:2(1-22)Online publication date: 29-Feb-2024
      • (2024) ImageVeriBypasser : An image verification code recognition approach based on Convolutional Neural Network Expert Systems10.1111/exsy.13658Online publication date: 25-Jun-2024
      • (2024)Image CAPTCHAs: When Deep Learning Breaks the MoldIEEE Access10.1109/ACCESS.2024.344297612(112211-112231)Online publication date: 2024
      • (2024)An Ecologically Valid Approach to Evaluating Online GatekeepersInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2398890(1-16)Online publication date: 12-Sep-2024
      • (2023)The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the “Real World”?Cancers10.3390/cancers1502035715:2(357)Online publication date: 5-Jan-2023
      • (2023)Neural network interpretation techniques for analysis of histological images of breast abnormalitiesGynecology10.26442/20795696.2022.6.20199024:6(529-537)Online publication date: 20-Jan-2023
      • (2023)CAPTCHA Recognition Using Deep Convolutional Neural Networks (DCNN)2023 Innovations in Power and Advanced Computing Technologies (i-PACT)10.1109/i-PACT58649.2023.10434845(1-8)Online publication date: 8-Dec-2023
      • (2023)Extended Research on the Security of Visual Reasoning CAPTCHAIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.323840820:6(4976-4992)Online publication date: Nov-2023
      • (2023)Exploring self-supervised learning in Multiview captcha recognition2023 IEEE 20th India Council International Conference (INDICON)10.1109/INDICON59947.2023.10440750(1106-1111)Online publication date: 14-Dec-2023
      • (2023)Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacksMultimedia Systems10.1007/s00530-023-01075-029:4(1865-1895)Online publication date: 30-Mar-2023
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media