POSTER: DeepCRACk: Using Deep Learning to Automatically CRack Audio CAPTCHAs.

A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a defensive mechanism designed to differentiate humans and computers to prevent unauthorized use of online services by automated attacks. They often consist of a visual or audio test that humans can perform easily but that bots cannot solve. However, with current machine learning techniques and open-source neural network architectures, it is now possible to create a self-contained system that is able to solve specific CAPTCHA types and outperform some human users. In this paper, we present a neural network that leverages Mozilla's open source implementation of Baidu's Deep Speech architecture; our model is currently able to solve the audio version of an open-source CATPCHA system (named SimpleCaptcha) with 98.8% accuracy. Our network was trained on 100,000 audio samples generated from SimpleCaptcha and can solve new SimpleCaptcha audio tests in 1.25 seconds on average (with a standard deviation of 0.065 seconds). Our implementation seems additionally promising because it does not require a powerful server to function and is robust to adversarial examples that target Deep Speech's pre-trained models.