Algorithms for clustering data
Algorithms for clustering data
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
WordNet: a lexical database for English
Communications of the ACM
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Using graphic turing tests to counter automated DDoS attacks against web servers
Proceedings of the 10th ACM conference on Computer and communications security
ARTiFACIAL: automated reverse turing test using FACIAL features
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Telling humans and computers apart automatically
Communications of the ACM - Information cities
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
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
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning attacks against the Asirra CAPTCHA
Proceedings of the 15th ACM conference on Computer and communications security
Distortion estimation techniques in solving visual CAPTCHAs
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 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
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
Re: CAPTCHAs: understanding CAPTCHA-solving services in an economic context
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
A novel architecture for the generation of picture based CAPTCHA
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
A survey and analysis of current CAPTCHA approaches
Journal of Web Engineering
FaceDCAPTCHA: Face detection based color image CAPTCHA
Future Generation Computer Systems
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Security researchers have, for a long time, devised mechanisms to prevent adversaries from conducting automated network attacks, such as denial-of-service, which lead to significant wastage of resources. On the other hand, several attempts have been made to automatically recognize generic images, make them semantically searchable by content, annotate them, and associate them with linguistic indexes. In the course of these attempts, the limitations of state-of-the-art algorithms in mimicking human vision have become exposed. In this paper, we explore the exploitation of this limitation for potentially preventing automated network attacks. While undistorted natural images have been shown to be algorithmically recognizable and searchable by content to moderate levels, controlled distortions of specific types and strengths can potentially make machine recognition harder without affecting human recognition. This difference in recognizability makes it a promising candidate for automated Turing tests [completely automated public Turing test to tell computers and humans apart (CAPTCHAs)] which can differentiate humans from machines. We empirically study the application of controlled distortions of varying nature and strength, and their effect on human and machine recognizability. While human recognizability is measured on the basis of an extensive user study, machine recognizability is based on memory-based content-based image retrieval (CBIR) and matching algorithms. We give a detailed description of our experimental image CAPTCHA system, IMAGINATION, that uses systematic distortions at its core. A significant research topic within signal analysis, CBIR is actually conceived here as a tool for an adversary, so as to help us design more foolproof image CAPTCHAs.