The academics, who say that they have reported their findings to Google, claim their system makes use of a fundamental design flaw in reCAPTCHA v2 that makes it easier for bots to solve the image puzzles. Finally, the third module performs the task of actually submitting the answers, checking if the challenge was successfully completed, and stopping if so. The module, which uses machine-learning code, spits out a JSON array with the object name, a confidence score, and the grid numbers to click in order to solve the puzzle. The first module gets the image itself as well as the challenge type – such as, what object needs to be clicked on – and what the layout of the grid is.Ī second module performs the task of actually identifying objects within the image.

To beat the system, the team built three different attack modules that each carry out a different task. This means websites using version two of Google's technology could be swarmed by bots, if this academic study works as claimed and is weaponized. This is effectively the difference between theorizing that the reCAPTCHA v2 system can be beaten using AI, and actively demonstrating how the verification filter can be beaten in the wild. The Louisiana team, however, we're told, took things a step further by performing the entire attack online and on-the-fly, rather than downloading the images and solving the challenge offline. Earlier this year, boffins at the University of Maryland showed how the audio version of the anti-bot tool could be thwarted, and previous efforts have demonstrated that the image-based filter could be defeated with deep-learning software. This is not the first time eggheads have shown how the widely used Google are-you-a-human system can be bypassed.

Google recaptcha bypass 2016