Next time you want to post a comment on a blog or use an online contact form, chances are you'll be confronted by a puzzle asking you to read some blurry and distorted text. Known as CAPTCHA, theses challenges are supposed to only be solvable by humans, in order to prevent unwanted bots from using web services.
However, their days as a human-only pursuit could be numbered: Google has built its own automated system that can beat CAPTCHAs with 99.8 percent accuracy.
The algorithm developed by Google researchers is being used by its Street View team to improve Google Maps, by helping to recognising characters in natural or blurry images — for example, the house numbers captured by the Street View cars in the course of gathering imagery for the mapping service.
According to the company, the algorithm can now accurately recognise 90 percent of street numbers, meaning Google Maps users looking for a particular building are likely to get a more specific result.
But, given the nature of that challenge, it turns out that the algorithm is also well-suited to solving CAPTCHA puzzles designed to fox spammers using bots for services like Gmail. As Google's engineers explain in a recently published paper, the algorithm has 99.8 percent accuracy rate when trying to decipher the hardest puzzles created by Google's own CAPTCHA service, reCAPTCHA.
The algorithm would be highly-prized by spammers, who are on the hunt for ways to automatically pass CAPTCHA puzzles.
While (optical character recognition) OCR technology is fairly mature, apparently reading characters from photographs is a "hard problem" to solve, according to Google, whose researchers have overcome it with the use of a "deep convolutional neural network that operates directly on the image pixels".
Despite the 99.8 percent accuracy rate of the algorithm, Google says reCAPTCHA isn't broken or ineffective, partly due to an update to the service last year, which added "advanced risk analysis techniques". The system considers the user's engagement with it before, during, and after they interact with it. Using this approach helps it determine whether a potential user is likely to be human or not, before deciding how difficult a puzzle to serve up.