A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Interactive Proofs and Document Image Analysis
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Telling humans and computers apart automatically
Communications of the ACM - Information cities
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Designing human friendly human interaction proofs (HIPs)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
IMAGINATION: a robust image-based CAPTCHA generation system
Proceedings of the 13th annual ACM international conference on Multimedia
Human computation
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
What's up CAPTCHA?: a CAPTCHA based on image orientation
Proceedings of the 18th international conference on World wide web
Balancing usability and security in a video CAPTCHA
Proceedings of the 5th Symposium on Usable Privacy and Security
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
Building segmentation based human-friendly human interaction proofs (HIPs)
HIP'05 Proceedings of the Second international conference on Human Interactive Proofs
SEMAGE: a new image-based two-factor CAPTCHA
Proceedings of the 27th Annual Computer Security Applications Conference
Auto-learning of SMTP TCP transport-layer features for spam and abusive message detection
LISA'11 Proceedings of the 25th international conference on Large Installation System Administration
Security and usability challenges of moving-object CAPTCHAs: decoding codewords in motion
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Breaking reCAPTCHAs with unpredictable collapse: heuristic character segmentation and recognition
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Fluxing botnet command and control channels with URL shortening services
Computer Communications
A survey and analysis of current CAPTCHA approaches
Journal of Web Engineering
The robustness of hollow CAPTCHAs
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
A User-friendly Image-Text Fusion CAPTCHA for Secure Web Services
Proceedings of International Conference on Information Integration and Web-based Applications & Services
DeepCAPTCHA: an image CAPTCHA based on depth perception
Proceedings of the 5th ACM Multimedia Systems Conference
FaceDCAPTCHA: Face detection based color image CAPTCHA
Future Generation Computer Systems
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We systematically study the design of image recognition CAPTCHAs (IRCs) in this paper. We first review and examine all existing IRCs schemes and evaluate each scheme against the practical requirements in CAPTCHA applications, particularly in large-scale real-life applications such as Gmail and Hotmail. Then we present a security analysis of the representative schemes we have identified. For the schemes that remain unbroken, we present our novel attacks. For the schemes for which known attacks are available, we propose a theoretical explanation why those schemes have failed. Next, we provide a simple but novel framework for guiding the design of robust IRCs. Then we propose an innovative IRC called Cortcha that is scalable to meet the requirements of large-scale applications. It relies on recognizing objects by exploiting the surrounding context, a task that humans can perform well but computers cannot. An infinite number of types of objects can be used to generate challenges, which can effectively disable the learning process in machine learning attacks. Cortcha does not require the images in its image database to be labeled. Image collection and CAPTCHA generation can be fully automated. Our usability studies indicate that, compared with Google's text CAPTCHA, Cortcha allows a slightly higher human accuracy rate but on average takes more time to solve a challenge.