CAPTCHA Is Dying. This Is How It’s Being Reinvented for the AI Age

by  Matt Burgess,

The goal of the Turing test is for a human to work out whether they’re communicating with another person or a machine. Computers have gotten better at imitating people but they’ve also improved at reversing the Turing test, tricking machines to think they’re human.

Back in the 2000s, when crude bots plagued the web, the solution turned out to be a variation of Turing’s test: the Completely Automated Public Turing Test to tell Computers and Humans Apart. Better known as the Captcha, the system allowed websites to determine between human and machine behaviour. Or that was the idea, anyway.

Completely Automated Public Turing Tests to tell Computers and Humans Apart (CAPTCHAs) appear to be heading toward obsolescence, with Vicarious demonstrating an algorithm that can solve the test with a recursive cortical network by using 5,000 times fewer training images than other methods. This and other advances are making CAPTCHAs less secure and less relevant, pushing researchers toward a rethinking of the test to strengthen it in the era of machine learning.

The battle between protecting websites from spammers and creating secure Captchas has become invisible. At the end of 2016, Google announced an Invisible reCaptcha that would use what it calls its Advanced Risk Analysis.

This system uses Google’s AI to look for signs of human behaviour. It runs in the background detecting movements of a mouse, how long it takes to click on a page, and removes the ‘I am not a robot box’ from webpages. The firm’s security blog says the Invisible system, which launched in March 2017, has “enabled millions of human users to pass through with zero click everyday”. It hasn’t given any more details on how the system works.

Nan Jiang at Bournemouth University in the U.K. has developed a mobile CAPTCHA named Tapcha, which builds on the traditional distorted text approach while still relying on human knowledge. “We use this approach to create the instruction,” Nan says. He believes a machine can only defeat this approach by understanding what is occurring in the image and coming to a conclusion on its own, a breakthrough that currently is very difficult to realize with artificial intelligence.  Read the full article

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