Researchers have developed an automated system for creating still and video images that can be identified by humans but not by computers. Such technology could be useful for Captcha systems, which were designed to keep “spambots”–or automated junk e-mail programs–from signing up for free online accounts.
The new technology uses simple images of a recognizable, moving figure, such as a running man or galloping horse, and converts them into blotches, hidden within a similarly blotchy scene. Computers are usually unable to detect the figure, but the human eye typically can.
Coined in 2000, “Captcha” stands for Completely Automated Public Turing test to tell Computers and Humans Apart. A typical Captcha system generates distorted text, often on a slightly cluttered background. The user must respond with the correct string of characters in order to access an online service, such as the account creation tool for a free e-mail address. But existing Captcha system don’t offer complete security–they are occasionally broken by security researchers and hackers. Captcha systems for Live Mail, Gmail, Yahoo!, Livejournal, and PayPal have all been cracked at one point. While the current systems are considered secure for now, most in the field agree it’s only a matter of time before they are broken again. Captcha system designers have to keep improving their methods to stay one step ahead of those who seek to circumvent them.
“The systems we all use today are relatively easy to break,” says Danny Cohen-Or, a researcher on the project and a professor of computer science at Tel Aviv University. “What we have developed is something that, with more effort, could be like the base of a stronger Captcha [system].”
Developed with researchers at the Indian Institute of Technology in Delhi, the National Cheng Kung University in Taiwan, and others from the University of Tel Aviv, the software was inspired by “gestalt,” or the idea of the whole being greater than the sum of the parts. Specifically, the software exploits the human ability to analyze a chaotic, fragmented scene to find a hidden figure.
The key was designing an adjustable system that could generate images that are easy enough for a human to identify, but too difficult for pattern-recognition software.
The software begins with a 3-D subject, such as a running dog. It converts the dog into a series of carefully generated black dots, which the researchers call “splats,” that take into account the dog’s silhouette and shape. To ensure that the subject isn’t too obvious, long, complicated shapes are broken into smaller parts, and the silhouettes are slightly deformed. The software then places the subject in a scene with more shapes, including some made of small pieces of the subject, to create added visual confusion. Videos are created as a series of still images.
The “emergent” images generated by the system were tested on three kinds of learning-based pattern recognition software. After training on a set of 30 emergent images, the systems were presented with other emergent images. The best of the three pattern recognition systems could only distinguish between a horse and a human 60 percent of the time. Humans, presented with the same task, answered correctly nearly 100 percent of the time. The software can also create images that are far more difficult for computers to interpret, but this would make it harder for humans to interpret, too. “It’s still not something that the big mass of users will be able to do,” Cohen-Or says.
Smaller design teams can now prototype and deploy faster.