How to "defeat" Google Audio Recapcha?

Overview of unCapcha - small tool to "pass thru" audio capcha 
26 October 2017   1079

Across the Internet, hundreds of thousands of sites rely on Google's reCaptcha system for defense against bots (in fact, Devpost uses reCaptcha when creating a new account). After a Google research team demonstrated a near complete defeat of the text reCaptcha in 2012, the reCaptcha system evolved to rely on audio and image challenges, historically more difficult challenges for automated systems to solve. Google has continually iterated on its design, releasing a newer and more powerful version as recently as just this year. Successfully demonstrating a defeat of this captcha system spells significant vulnerability for hundreds of thousands of popular sites.

What is unCapcha?

UnCaptcha system has attack capabilities written for the audio captcha. Using browser automation software, we can interact with the target website and engage with the captcha, parsing out the necessary elements to begin the attack. We rely primarily on the audio captcha attack - by properly identifying spoken numbers, we can pass the reCaptcha programmatically and fool the site into thinking our bot is a human. Specifically, unCaptcha targets the popular site Reddit by going through the motions of creating a new user, although unCaptcha stops before creating the user to mitigate the impact on Reddit.

Background

Google's reCaptcha system uses an advanced risk analysis system to determine programmatically how likely a given user is to be a human or a bot. It takes into account your cookies (and by extension, your interaction with other Google services), the speed at which challenges are solved, mouse movements, and (obviously) how successfully you solve the given task. As the system gets increasingly suspicious, it delivers increasingly difficult challenges, and requires the user to solve more of them. Researchers have already identified minor weaknesses with the reCaptcha system - 9 days of legitimate (ish) interaction with Google's services is usually enough to lower the system's suspicion level significantly.

How it works

The format of the audio captcha is a varied-length series of numbers spaced out read aloud at varied speeds, pitches, and accents through background noise. To attack this captcha, the audio payload is identified on the page, downloaded, and automatically split by locations of speech.

From there, each number audio bit is uploaded to 6 different free, online audio transcription services (IBM, Google Cloud, Google Speech Recognition, Sphinx, Wit-AI, Bing Speech Recognition), and these results are collected. Developers ensemble the results from each of these to probabilistically enumerate the most likely string of numbers with a predetermined heuristic. These numbers are then organically typed into the captcha, and the captcha is completed. From testing, team has seen 92%+ accuracy in individual number identification, and 85%+ accuracy in defeating the audio captcha in its entirety.

Students to Beat Google’s Machine-Learning Code

Student programmers' image classification algorithm successfully identifies the object in 93% of cases
13 August 2018   395

Developers-students from Fast.ai which organize free online computer training courses have created an image classification algorithm that successfully identifies the object in 93% of cases and copes with it faster than a similar Google algorithm with a similar configuration. The authors argue that "the creation of breakthrough technologies is not just for big companies". This is reported by MIT Technology Review.

When evaluating performance, the DAWNBench test was used, which calculates the speed and cost of teaching the neural network. During the Fast.ai experiment, the neural network was launched on 16 virtual AWS nodes, each contained 8 NVIDIA V100 graphics cards. This configuration achieved accuracy of 93% in 18 minutes, and the cost of machine time was estimated at $ 40. The result of Fast.ai is faster than the development of Google engineers by 40%, but the corporation uses its own clusters TPU Pod, so the comparison is not entirely objective.

The developers used the PyTorch Python library, as well as their own development - fastai. They were able to achieve this learning speed with the new method of cropping images from the ImageNet dataset: instead of square pictures, they began to use rectangular:

Fast AI
Fast AI

State-of-the-art results are not the exclusive domain of big companies. These are the obvious, dumb things that many researchers wouldn’t even think to do.
 

Jeremy Howard

Founder, Fast.AI

The authors tried to make the project accessible to everyone, so they simplified its infrastructure, refusing to use distributed computing systems and containers. To implement it, developers teamed up with engineers from the innovative division of the Pentagon (DIU) to release software to quickly create and support distributed models on AWS.