Classifying Images Collected on the World Wide Web
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
The Journal of Machine Learning Research
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
Proceedings of the 14th ACM conference on Computer and communications security
Usability of CAPTCHAs or usability issues in CAPTCHA design
Proceedings of the 4th symposium on Usable privacy and security
Machine learning attacks against the Asirra CAPTCHA
Proceedings of the 15th ACM conference on Computer and communications security
A low-cost attack on a Microsoft captcha
Proceedings of the 15th ACM conference on Computer and communications security
CAPTCHA Security: A Case Study
IEEE Security and Privacy
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Distortion estimation techniques in solving visual CAPTCHAs
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A survey of image spamming and filtering techniques
Artificial Intelligence Review
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The landscape of the World Wide Web today consists of a vast amount of services. While most of them are offered for free, the service providers prohibit their malicious usage by automated scripts. To enforce this policy, CAPTCHAS have emerged as a reliable method to setup a Turing test to distinguish between human and computers. Image recognition CAPTCHAS as one type of CAPTCHAS promise high human success rates. In this paper however, we develop an successful approach to attack this type of Captcha. To evaluate our attack we implemented a publicly available tool, which delivers promising results for the HumanAuth Captcha and others. Based upon our findings we propose several techniques for improving future versions of image recognition CAPTCHAS.