The Failure of Noise-Based Non-continuous Audio Captchas

@article{Bursztein2011TheFO,
  title={The Failure of Noise-Based Non-continuous Audio Captchas},
  author={Elie Bursztein and Romain Beauxis and Hristo S. Paskov and Daniele Perito and Celine Fabry and John C. Mitchell},
  journal={2011 IEEE Symposium on Security and Privacy},
  year={2011},
  pages={19-31}
}
CAPTCHAs, which are automated tests intended to distinguish humans from programs, are used on many web sites to prevent bot-based account creation and spam. To avoid imposing undue user friction, CAPTCHAs must be easy for humans and difficult for machines. However, the scientific basis for successful CAPTCHA design is still emerging. This paper examines the widely used class of audio CAPTCHAs based on distorting non-continuous speech with certain classes of noise and demonstrates that virtually… 
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References

SHOWING 1-10 OF 32 REFERENCES
Breaking Audio CAPTCHAs
TLDR
This work analyzed the security of current audio CAPTCHAs from popular Web sites by using AdaBoost, SVM, and k-NN, and achieved correct solutions for test samples with accuracy up to 71%.
Audio CAPTCHA: Existing solutions assessment and a new implementation for VoIP telephony
TLDR
This paper develops and implements a new audio CAPTCHA, which is suitable for SIP-based VoIP telephony, and is tested against users and bots and demonstrated to be efficient.
A low-cost attack on a Microsoft captcha
TLDR
It is shown that CAPTCHAs that are carefully designed to be segmentation-resistant are vulnerable to novel but simple attacks, including the schemes designed and deployed by Microsoft, Yahoo and Google.
How Good Are Humans at Solving CAPTCHAs? A Large Scale Evaluation
TLDR
Evidence from a week’s worth of eBay captchas suggests that the solving accuracies found in the study are close to real-world values, and that improving audioCaptchas should become a priority, as nearly 1% of all captchAs are delivered as audio rather than images.
A reverse turing test using speech
TLDR
This paper describes a Reverse Turing Test using speech and presents a test that depends on the fact that human recognition of distorted speech is far more robust than automatic speech recognition techniques.
Breaking Visual CAPTCHAs with Naive Pattern Recognition Algorithms
  • Jeff Yan, A. E. Ahmad
  • Computer Science
    Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007)
  • 2007
TLDR
This paper document how most such visual CAPTCHAs provided at Captchaservice.org, a publicly available web service for CAPTCHA generation, were broken with a near 100% success rate by their novel attacks.
Decaptcha: breaking 75% of eBay audio CAPTCHAs
CAPTCHA tests aim at preventing attackers from performing automatic website registration. In this paper we show that our prototype Decaptcha is able to successfully break 75% of eBay audio captchas.
CAPTCHA: Using Hard AI Problems for Security
TLDR
This work introduces captcha, an automated test that humans can pass, but current computer programs can't pass; any program that has high success over a captcha can be used to solve an unsolved Artificial Intelligence (AI) problem; and provides several novel constructions of captchas, which imply a win-win situation.
Machine learning attacks against the Asirra CAPTCHA
  • P. Golle
  • Computer Science
    IACR Cryptol. ePrint Arch.
  • 2008
TLDR
A classifier which is 82.7% accurate in telling apart the images of cats and dogs used in Asirra, which is significantly higher than the estimate of 0.2% given in [7] for machine vision attacks.
Recognizing objects in adversarial clutter: breaking a visual CAPTCHA
  • Greg Mori, Jitendra Malik
  • Computer Science
    2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
  • 2003
TLDR
Efficient methods based on shape context matching are developed that can identify the word in an EZ-Gimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time.
...
1
2
3
4
...