Situated Cognition: On Human Knowledge and Computer Representations
Situated Cognition: On Human Knowledge and Computer Representations
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Telling humans and computers apart automatically
Communications of the ACM - Information cities
IMAGINATION: a robust image-based CAPTCHA generation system
Proceedings of the 13th annual ACM international conference on Multimedia
AICT-ICIW '06 Proceedings of the Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services
CSIFT: A SIFT Descriptor with Color Invariant Characteristics
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Selection and Fusion of Color Models for Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A New Approach for Color-Based Object Recognition with Fusion of Color Models
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 3 - Volume 03
Machine learning attacks against the Asirra 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
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Robust image retrieval based on color histogram of local feature regions
Multimedia Tools and Applications
TurKit: human computation algorithms on mechanical turk
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Attacks and design of image recognition CAPTCHAs
Proceedings of the 17th ACM conference on Computer and communications security
Color lines: image specific color representation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
On-Line, incremental learning of a robust active shape model
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Motion Objects Detection Based on Higher Order Statistics and HSV Color Space
ICM '11 Proceedings of the 2011 International Conference of Information Technology, Computer Engineering and Management Sciences - Volume 03
Combining color and shape information for illumination-viewpoint invariant object recognition
IEEE Transactions on Image Processing
Face Recognition Challenge: Object Recognition Approaches for Human/Avatar Classification
ICMLA '12 Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 02
Learning Visual Features for the Avatar Captcha Recognition Challenge
ICMLA '12 Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 02
FaceDCAPTCHA: Face detection based color image CAPTCHA
Future Generation Computer Systems
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Over the past decade, text-based CAPTCHA (TBC) have become popular in preventing adversarial attacks and spam in many websites and applications including emails services, social platforms, web-based market places, and recommendation systems. However, in addition to several problems with TBC, it has become increasingly difficult to solve in recent years, to keep up with OCR technologies. Image-based CAPTCHA (IBC), on the other hand, is a relatively new concept that promises to overcome key limitations of TBC. In this paper we present an innovative IBC, DeepCAPTCHA, based on design guidelines, psychological theory and empirical experiments. DeepCAPTCHA exploits the human ability of depth preception. In our IBC users should arrange 3D objects in terms of size (or depth). In our framework for DeepCAPTCHA, we automatically mine 3D models, and use a human-machine Merge Sort algorithm to order these unknown objects. We then create new appearances for these objects at multiplication factor of 200, and present these new images to the end-users for sorting (as CAPTCHA tasks). Humans are able to apply their rapid and reliable object recognition and comparison (arise from years experience with the physical environment) to solve DeepCAPTCHA, while machines are still unable to complete these tasks. Experimental results show that humans can solve DeepCAPTCHA with a high accuracy (~84%) and ease, while machines perform dismally.