Jeff Yan

AAAI Conference 2010 Conference Paper

Collusion Detection in Online Bridge

• Jeff Yan

Collusion is a major unsolved security problem in online bridge: by illicitly exchanging card information over the telephone, instant messenger or the like, cheaters can gain huge advantages over honest players. It is very hard if not impossible to prevent collusion from happening. Instead, we motivate an AI-based detection approach and discuss its challenges. We challenge the AI community to create automated methods for detecting collusive traces left in game records with an accuracy that can be achieved by human masters.

IJCAI Conference 2009 Conference Paper

• Jeff Yan
• Su-Yang Yu

CAPTCHA has been widely deployed by commercial web sites as a security technology for purposes such as anti-spam. A common approach to evaluating the robustness of CAPTCHA is the use of machine learning techniques. Critical to this approach is the acquisition of an adequate set of labeled samples, on which the learning techniques are trained. However, such a sample labeling task is difficult for computers, since the strength of CAPTCHAs stems exactly from the difficulty computers have in recognizing either distorted texts or image contents. Therefore, until now, researchers have to manually label their samples, which is tedious and expensive. In this paper, we present Magic Bullet, a computer game that for the first time turns such sample labeling into a fun experience, and that achieves a labeling accuracy of as high as 98% for free. The game leverages human computation to address a task that cannot be easily automated, and it effectively streamlines the evaluation of CAPTCHAs. The game can also be used for other constructive purposes such as 1) developing better machine learning algorithms for handwriting recognition, and 2) training people’s typing skills.