Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Ima e Orientation Detection with Support Vector Machines
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Boosting Image Orientation Detection with Indoor vs. Outdoor Classification
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Detecting image orientation based on low-level visual content
Computer Vision and Image Understanding
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Designing human friendly human interaction proofs (HIPs)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic image orientation determination with natural image statistics
Proceedings of the 13th annual ACM international conference on Multimedia
Automated image-orientation detection: a scalable boosting approach
Pattern Analysis & Applications
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
Proceedings of the 14th ACM conference on Computer and communications security
ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
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
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
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
Automatic image orientation detection
IEEE Transactions on Image Processing
Balancing usability and security in a video CAPTCHA
Proceedings of the 5th Symposium on Usable Privacy and Security
Games for sketch data collection
Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling
Proceedings of the 2nd ACM workshop on Security and artificial intelligence
Scene tagging: image-based CAPTCHA using image composition and object relationships
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Sketcha: a captcha based on line drawings of 3D models
Proceedings of the 19th international conference on World wide web
Attacks and design of image recognition CAPTCHAs
Proceedings of the 17th ACM conference on Computer and communications security
Web robot detection techniques: overview and limitations
Data Mining and Knowledge Discovery
3D drag-n-drop CAPTCHA enhanced security through CAPTCHA
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume I
SEMAGE: a new image-based two-factor CAPTCHA
Proceedings of the 27th Annual Computer Security Applications Conference
Do cognitive styles of users affect preference and performance related to CAPTCHA challenges?
CHI '12 Extended Abstracts on Human Factors in Computing Systems
CAPTCHA suitable for smartphones
ICT-EurAsia'13 Proceedings of the 2013 international conference on Information and Communication Technology
A survey and analysis of current CAPTCHA approaches
Journal of Web Engineering
DeepCAPTCHA: an image CAPTCHA based on depth perception
Proceedings of the 5th ACM Multimedia Systems Conference
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We present a new CAPTCHA which is based on identifying an image's upright orientation. This task requires analysis of the often complex contents of an image, a task which humans usually perform well and machines generally do not. Given a large repository of images, such as those from a web search result, we use a suite of automated orientation detectors to prune those images that can be automatically set upright easily. We then apply a social feedback mechanism to verify that the remaining images have a human-recognizable upright orientation. The main advantages of our CAPTCHA technique over the traditional text recognition techniques are that it is language-independent, does not require text-entry (e.g. for a mobile device), and employs another domain for CAPTCHA generation beyond character obfuscation. This CAPTCHA lends itself to rapid implementation and has an almost limitless supply of images. We conducted extensive experiments to measure the viability of this technique.