CAPTCHA: Using Hard AI Problems for Security

By Luis von Ahn, Manuel Blum, Nicholas Hopper, J. Langford. International Conference on the Theory and Application of Cryptographic Techniques, 2003

1,710 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/3d9e24e36725f3a138452636abb481d1d601ed89

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Citations in the corpus

How to Write Science Questions that Are Easy for People and Hard for Computers (2016, background): “The success of CAPTCHA’s (von Ahn et al. 2003) shows that it is possible to automatically generate images that are easy for people to interpret and hard for computers; however, that is an unusual case. Weston et al. (2015) propose to build a system that uses a world model and a linguistic model to g”
The socialbot network: when bots socialize for fame and money (2011, background): “An adversary, however, can often circumvent this countermeasure by using di.erent techniques such as automated analysis using optical character recognition [6], exploiting botnets to trick infected victims into manually solving CAPTCHAs [5, 12], reusing session IDs of known CAPTCHAs [18], cracki”
The Winograd Schema Challenge (2011, background): “One possibility is something like the captcha (von Ahn et al 2003).”
Securing Web Service by Automatic Robot Detection (2006, methodology): “CAPTCHA (CompletelyAutomatedPublic Turing test to tell Computers andHumansApart) is a test consisting of distorted images or sounds, sometimes with instructive description, that are designed to be difficult for robots to decipher [8].”
Shilling recommender systems for fun and profit (2004, background): “For instance, an attack on a system that employs anti-automation techniques such as CAPTCHAs may have a far higher cost than an attack on a system that does not [21].”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “Work by Von Ahn’s group has explored the design of game-based interfaces to extract useful data from human players without monetary rewards [182, 185, 183, 184, 186].”
SoK: The Evolution of Sybil Defense via Social Networks (2013, background): “After all this effort, Section 7 greets us with a sobering result: in spite of their sophistication, state of the art sybil defense protocols are helpless against very crude real−life sybil attacks.”
I am Robot: (Deep) Learning to Break Semantic Image CAPTCHAs (2016, background): “The first publication to rigorously explore the use of automated tests for security [1], also proposed the use of distorted images of animals for creating challenges.”
Botz-4-sale: surviving organized DDoS attacks that mimic flash crowds (2005, methodology): “We focus on graphical tests [47], though KillBots works with other types of reverse Turing tests.”
Recommender Systems (2016, neutral): “CAPTCHAs have been designed [619] to tell humans and computers apart.”
SybilGuard: Defending Against Sybil Attacks via Social Networks (2006, background): “require human efforts, such as CAPTCHAs [3], can be reposted on the adversary’s web site to be solved by other users seeking access to the site.”