We present a technique for using content-based video labeling as a CAPTCHA task. Our CAPTCHAs are generated from YouTube videos, which contain labels (tags) supplied by the person that uploaded the video. They are graded using a video’s tags, as well as tags from related videos. In a user study involving 184 participants, we were able to increase the human success rate on our video CAPTCHA from roughly 70 % to 90%, while keeping the success rate of a tag frequency-based attack fixed at around 13%. Through a different parameterization of the challenge generation and grading algorithms, we were able to reduce the success rate of the same attack to 2%, while still increasing the human success rate from 70% to 75%. The usability and security of our video CAPTCHA appears to be comparable to existing CAPTCHAs, and a majority of participants (60%) indicated that they found the video CAPTCHAs more enjoyable than traditional CAPTCHAs in which distorted text must be transcribed.

A Completely Automated Public Turing test to tell Computer and Humans Apart (CAPTCHA) is a variation of the Turing test, in which a challenge is used to distinguish humans from computers (‘bots’) on the internet. They are commonly used to prevent the abuse of online services; for example, malicious users have written automated programs that sign up for thousands of free email accounts and send SPAM messages. A number