Digital Image Warping
Telling humans and computers apart automatically
Communications of the ACM - Information cities
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IMAGINATION: a robust image-based CAPTCHA generation system
Proceedings of the 13th annual ACM international conference on Multimedia
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
Proceedings of the 14th ACM conference on Computer and communications security
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
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
Exploiting the human-machine gap in image recognition for designing CAPTCHAs
IEEE Transactions on Information Forensics and Security
Distortion estimation techniques in solving visual CAPTCHAs
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SEMAGE: a new image-based two-factor CAPTCHA
Proceedings of the 27th Annual Computer Security Applications Conference
An empirical study on efficiency and effectiveness of localized vs. Latin-based CAPTCHA challenges
Proceedings of the 17th Panhellenic Conference on Informatics
A novel gesture-based CAPTCHA design for smart devices
BCS-HCI '13 Proceedings of the 27th International BCS Human Computer Interaction Conference
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In this paper, we propose a new form of image-based CAPTCHA we term "scene tagging". It tests the ability to recognize a relationship between multiple objects in an image that is automatically generated via composition of a background image with multiple irregularly shaped object images, resulting in a large space of possible images and questions without requiring a large object database. This composition process is accompanied by a carefully designed sequence of systematic image distortions that makes it difficult for automated attacks to locate/identify objects present. Automated attacks must recognize all or most objects contained in the image in order to answer a question correctly, thus the proposed approach reduces attack success rates. An experimental study using several widely-used object recognition algorithms (PWD-based template matching, SIFT, SURF) shows that the system is resistant to these attacks with a 2% attack success rate, while a user study shows that the task required can be performed by average users with a 97% success rate.