Collection of a Dataset

The collection of Data is the first and essential step in training a Machine Learning Model. It is no different here. First, we need to find a dataset with many CAPTCHA images. The dataset needs to be diverse to ensure the model would be able to solve any CAPTCHA it is given.The collection of CAPTCHA images is not that easy of a feat. Finding a legal way to acquire the datasets is quite an involved process, and if you want to scrape them from websites, you should be informed that doing it without permission might be illegal and it is also unethical. So, we need to resort to using open-source datasets.

A dataset that can be used is a small Dataset from Kaggle. It is sufficient for learning about Captchas. You can find it here.

A dataset is effectively a folder with images and labels. You just need to mention the path, and it is as simple as that.

How to Break a CAPTCHA System with Machine Learning?

CAPTCHA, short for Completely Automated Public Turing Test to Tell Computers and Humans Apart, is a revolutionary technology that helps identify humans from bots and saves your site from malicious intentions. But this technology has begun to show its age. Captcha was supposed to be a robust system, but artificial intelligence is driving it almost useless. To break a Captcha, we require a machine-learning model which we need to train. After its training, all that is required is to feed the model any CAPTCHA you want, which it will solve for you.

 

Through this article, we will explore how one can break a CAPTCHA system with the help of machine learning. We will discuss in detail the complete process. Besides, we will also share the limitations of this approach and the ethical and moral issues that need to be considered while attempting this. This should be remembered that our intention behind breaking CAPTCHA should be to educate ourselves and highlight the incapability of the system to filter out non-humans. But CAPTCHAs are the things saving sites from malicious attacks, and they are effectively safeguarding the internet. So, using bots to break CAPTCHAs on websites without permission is unethical at best and also illegal, depending on your location.

Similar Reads

Collection of a Dataset

The collection of Data is the first and essential step in training a Machine Learning Model. It is no different here. First, we need to find a dataset with many CAPTCHA images. The dataset needs to be diverse to ensure the model would be able to solve any CAPTCHA it is given.The collection of CAPTCHA images is not that easy of a feat. Finding a legal way to acquire the datasets is quite an involved process, and if you want to scrape them from websites, you should be informed that doing it without permission might be illegal and it is also unethical. So, we need to resort to using open-source datasets....

Preprocessing the Images

The second step in training the model is preprocessing. With Preprocessing, we can feed the model data it actually needs. Preprocessing consists of various steps like Cropping, Noise Reduction, Greyscale, and much more which allows us to create a better Machine learning Model. Various processes are done to transform the image while Preprocessing. These include Grayscale Conversion, Resizing, etc. These steps make the input images simpler which helps the computer identify patterns in these images....

Training the machine learning model

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Testing the Machine Learning Model

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Generating Adversarial Examples

After preprocessing comes the hard part, we can use various machine learning algorithms and techniques to break CAPTCHA. Convolutional Neural Networks(CNNs) and Recurrent Neural Networks(RNNs) can both be used to break CAPTCHA. While CNNs are a perfect match for image recognition and are very effective while recognizing images, RNNs can process sequential data very proficiently, suitable for things like audio-based CAPTCHA. Preprocessed images can be fed to the Machine Learning model. Using clever mathematics, the model will start to recognize patterns in the provided images and it adjusts its weights and biases and learns....

Conclusion

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