Breaking CAPTCHA Systems with Machine Learning in Just 15 Minutes

Everyone finds CAPTCHAs, the security measures involving typing text from an image, quite irritating. Initially designed to verify human presence and deter automated access, CAPTCHAs are increasingly susceptible to machine learning’s power.

Inspired by Adrian Rosebrock’s book, “Deep Learning for Computer Vision with Python,” I explored bypassing a popular WordPress CAPTCHA plug-in using machine learning.

Table of Contents

The Experiment

For this experiment, we targeted the widely used open-source CAPTCHA plugin, “Really Simple CAPTCHA,” readily available with its source code. The aim is to compromise this CAPTCHA in under 15 minutes, a playful technical challenge rather than a critique.

The Target: “Really Simple CAPTCHA” produces simple, four-letter CAPTCHA images. Verifying this in the PHP source code revealed that it uses a mix of four fonts, purposely excluding the letters “O” and “I” to prevent ambiguity. This leaves 32 possible characters for recognition.

Elapsed Time: 2 minutes

Tools for the Job

To tackle the CAPTCHA, we employed:

Python 3: A versatile programming language suitable for machine learning and computer vision.
OpenCV: A robust library for image processing, with Python compatibility.
Keras: A user-friendly deep learning framework that simplifies defining, training, and using neural networks.
TensorFlow: Google’s library underpinning Keras’s neural network operations.

Crafting the Dataset

Access to the plugin’s source code allowed us to generate 10,000 CAPTCHA images with their solutions, providing ample training data.

Elapsed Time: 5 minutes

Simplifying the Challenge

CAPTCHAs consist of four characters. Training the neural network on separate character images simplifies the task, reducing required training time and computational power. Automatically splitting image regions into individual letters using OpenCV’s findContours() can facilitate this.

Thresholding: Convert the image to black-and-white to highlight continuous regions.
Contour Detection: Use OpenCV’s findContours() to detect pixel blobs.
Extraction: Save each region as a separate image file.

Handling Overlapping Letters: Overlapping characters can be split by assessing the aspect ratio, dividing when width significantly exceeds height.

Neural Network Training

Leveraging a simple convolutional neural network (CNN) architecture, training became swift. Given the simpler nature of letter recognition versus more intricate image identification, two convolutional layers and two fully connected layers sufficed.

Architecture Setup: Define the compact CNN architecture using Keras.
Training: Achieve near-perfect accuracy with ten iterations over the dataset.

Elapsed Time: 15 minutes

Deploying the Model

Deploying the trained model to solve real CAPTCHAs involved:

Capture Image: Retrieve CAPTCHA images from practical implementations.
Segmentation: Utilize the same segmentation techniques to extract individual letters.
Prediction: Have the neural network predict each character.
Completion: Submit the predicted text as a CAPTCHA solution.

Visual Explanation:

Here’s a conceptual image showing the process:

A depiction of solving CAPTCHAs using a neural network, illustrating segmentation and prediction phases with labels "Image Segmentation" and "Character Prediction." Include a flow showing a CAPTCHA image getting split into characters, each processed by the neural network, and a final predicted CAPTCHA text. Use simple icons to represent neural network layers, segmentation tools, and a CAPTCHA image.

Conclusion

Breaking CAPTCHAs with machine learning is feasible and efficient with the right tools and approach. However, I encourage using this knowledge responsibly to enhance systems rather than exploit them.

Try it Yourself

For hands-on experience, access the code and datasets used here to replicate the steps discussed. Remember, ethical considerations come first!

Feel free to modify the images or request additional details as needed!