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Buzzers
Briefly describes how to use machine learning to bypass the verification code of the E-ZPass New York website.
Let's break through the world's most popular WordPress CAPTCHA plugin. Everyone hates CAPTCHAs — you're always asked to enter the text in those annoying images before you can access a website. The purpose of these codes is to verify that you're a real person and not a bot filling out forms automatically. However, with the rise of deep learning and computer vision, they are now often easily bypassed.
I'm currently reading Adrian Rosebrock's excellent book, *Python Computer Vision: Deep Learning*. In it, he briefly describes how he used machine learning to bypass the CAPTCHA on the E-ZPass New York website. This inspired me to explore whether I could crack an open-source CAPTCHA system.
I went to the WordPress plugin repository and searched for "verification code." The top result was a plugin called "Real Simple Verification Code," which has over 1 million active installations. What's even better is that it's open source! Since we have the source code that generates the CAPTCHA, this should be easier to crack. To make it more challenging, I decided to give myself a time limit — can I crack this system in 15 minutes? Let's try!
Important note: This is not a criticism of the "Real Simple Verification Code" plugin or its author. In fact, the author himself admits that the plugin is no longer secure and suggests using a different one. This is just a quick technical challenge for fun. If you're still using it, maybe consider switching to a more secure alternative.
The Challenge Begins
To create an offensive plan, let's first look at what type of image this plugin generates. On the demo site, we see something like this:
[Image of CAPTCHA]
Okay, so the CAPTCHA seems to be four letters. Let's check the PHP source code:
[Code snippet showing the generation of a four-letter CAPTCHA using random fonts, avoiding 'O' and 'I']
Yes, it produces a four-letter code using four different fonts. It avoids 'O' and 'I' to prevent confusion. That gives us 32 possible characters. No problem! So far, 2 minutes have passed.
Our Toolset
Before moving forward, let's talk about the tools we'll use:
- **Python 3**: A powerful programming language with great libraries for machine learning and computer vision.
- **OpenCV**: A popular computer vision library that we'll use to process the CAPTCHA image.
- **Keras**: A high-level deep learning framework written in Python, which allows us to define, train, and use neural networks with minimal code.
- **TensorFlow**: Google's machine learning library, which Keras uses under the hood for computations.
Now, back to the challenge!
Creating Our Dataset
To train any machine learning model, we need a dataset. For cracking a CAPTCHA, our training data should look like this:
[Images of CAPTCHA samples]
This is the only part where I won't provide sample code. I don't want you to go to a black hat site. However, I've generated 10,000 images for you to repeat my results. So far, 5 minutes have passed.
Simplifying the Problem
Now that we have the training data, we can directly train a neural network. But to make it easier, we can split each CAPTCHA into individual letters. This way, we only need to train the model to recognize one character at a time.
[Image showing letter segmentation]
However, the letters are randomly placed, making it harder to segment them. I don't have time to manually split 10,000 images in Photoshop. Plus, splitting into equal blocks won't work because the letters are positioned differently.
But there's a solution. Using OpenCV, we can detect contours — groups of pixels with the same color. Then, we extract each letter as a separate image. Sometimes, letters overlap, but we can split those areas into two parts.
[Image showing contour detection and letter extraction]
Once we have all the individual letters, we can organize them into folders by character. Here's what my "W" folder looks like after processing 10,000 CAPTCHA images:
[Image of W letters]
So far, 10 minutes have passed.
Training the Neural Network
Since we're only identifying single letters, we don't need a complex model. We'll use a simple convolutional neural network with two convolutional layers and two fully connected layers.
[Code snippet showing CNN architecture]
Defining this with Keras takes just a few lines of code. Now, we can start training.
After 10 epochs, we achieved nearly 100% accuracy. We did it! The time has passed: 15 minutes.
Cracking the Real CAPTCHA
Now that we have a trained model, it's easy to use it to crack real CAPTCHAs:
1. Download the CAPTCHA image from the WordPress site.
2. Split it into four individual letters using the same method as before.
3. Use the neural network to predict each letter.
4. Combine the predictions to get the full code.
Here's how our model decodes a real CAPTCHA:
[Animated GIF showing CAPTCHA decoding]
Or from the command line:
[Command line output showing successful prediction]
This was a fun and educational challenge. While it's interesting to see how easy some CAPTCHAs can be cracked, it also highlights the importance of using more secure alternatives.