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ML Models for Auto-Detecting and Bypassing CAPTCHAs

· 8 min read
Oleg Kulyk

ML Models for Auto-Detecting and Bypassing CAPTCHAs

The internet is a big and always expanding space that contains information on almost every possible issue.

Nevertheless, most of this data is hidden behind websites that use CAPTCHAs to prevent web bots from getting their content.

CAPTCHA bypassing is becoming more prevalent, as they can help to get data from the websites for different purposes.

This article examines various CAPTCHA types and how to bypass CAPTCHA in web scraping.

What are CAPTCHAs?

CAPTCHA is an acronym that is short for "Completely Automated Public Turing Test to Tell Computers and Humans Apart."

It is generally a challenge-and-response test used to differentiate between humans and bots.

Websites use CAPTCHAs to hinder malicious activities such as automated bot attacks, spam, and data theft.

Different CAPTCHAs are designed, each presenting a different problem for automatic processing technologies.

Here's a breakdown of the most common types:

  • Text CAPTCHAs: These representations often are in non-standard text that requires users to decipher and type in accurately.

  • Image CAPTCHAs: These tasks involve identifying objects within an image, such as a photographed traffic light.

  • Math-based CAPTCHAs: These give the user a straightforward math problem to solve and provide a text field where to insert an answer, such as "What is 5 + 7?"

  • Audio CAPTCHAs: Depending on the content of the audio, these tasks involve listening to an audio clip and transcribing the words or answering a question.

  • Checkbox CAPTCHAs: They may include checking a box indicating that the user is not a robot in a reCaptcha instance.

  • Interactive CAPTCHAs: The users must go through a series of puzzles or games to prove to the system that they are real human beings.

Reasons Why Websites Use CAPTCHAs

Websites utilize CAPTCHAs for several reasons, primarily aimed at enhancing security and protecting against various forms of abuse and malicious activities:

  • Security: CAPTCHA is usually the first line of defense against bots trying to undermine security by utilizing bugs or performing malicious activities, e.g., spam, data scraping, and account takeover.

  • Spam Prevention: Sites can stop receiving spam messages, comments, and fake registration created by bots by using the CAPTCHA solution before creating forms or posting any blog.

  • User Verification: CAPTCHAs help ensure that a person who attempts to enter sensitive or restricted website sections is a human, not a computer, thus preventing such unauthorized access or fraud.

  • Data Protection: Websites can install a CAPTCHA to prevent bots from carrying out unauthorized scraping and stealing personal information.

  • Content Protection: CAPTCHA, which is used to protect copyrighted work that comprises digital assets or intellectual property, stops automated scraping, copying, or unauthorized distribution.

  • Fraud Prevention: CAPTCHAs are used in online shopping, account registration, and password recovery to stop automated fraud attempts, such as brute force attacks and credential stuffing.

  • Compliance: In certain sectors, regulatory guidelines demand the utilization of CAPTCHA or its analogs to enhance security and data confidentiality.

  • Monetization: Some web pages could use CAPTCHA to get paid by CAPTCHA-solving services, and some visitors could see ads while working with CAPTCHA.

On the contrary, although captchas are valuable in securing websites from illegal acts, their overuse could deter some legitimate users.

Users must spend time solving CAPTCHAs, which might irritate them, especially those who are visually impaired or have other disabilities preventing them from solving the riddle.

How to Bypass CAPTCHA

  1. Manual Solving: Hiring humans to solve CAPTCHA problems individually is very efficient but labor-consuming. An example of this CAPTCHA bypass technique is when solving services, employing people who get paid to fulfill CAPTCHA challenges on behalf of clients.

  2. OCR (Optical Character Recognition): OCR algorithms are built to identify and remove text from images. The OCR procedures that can be applied make it possible to automate recognizing text-based CAPTCHAs, which might contain distorted characters.

  3. Deep Learning Models: CNNs are examples of deep learning models that have shown their efficiency in solving CAPTCHA. The models can learn to pinpoint patterns and define image features in the CAPTCHA sample to distinguish between correct and distorted text.

  4. Reinforcement Learning: Reinforcement learning techniques can be used as iterative units to upgrade CAPTCHA-solving algorithms automatically. Equipped with the mechanism of rewarding the right solutions and punishing the wrong ones, the model can learn from its previous mistakes and devise a suitable strategy for winning the game.

Machine Learning Models for Auto-Detecting and Bypassing CAPTCHAs

Machine learning models have increasingly become a core among CAPTCHA bypass techniques, automating the means of overcoming these challenges.

These models utilize massive labeled CAPTCHA picture datasets, which help them learn different patterns and features that separate various categories of challenges.

After being trained, the models can be set up to avoid high-precision CAPTCHAs in web scraping.

There are two main approaches to using machine learning models for bypassing CAPTCHAs:

  • Classification models: These models can classify CAPTCHAs into various categories. Once the category of CAPTCHA is detected, the model can apply a set of methods to solve it.

  • Regression models: These models are designed to learn the solution of CAPTCHA. The model receives the image of the CAPTCHA as the input image and outputs the predicted solution.

Different Machine Learning Models for Bypassing CAPTCHAs include the following:

Convolutional Neural Networks (CNNs)

CNNs have become the most threatening weapon for avoiding CAPTCHAs in web scraping, as they can automatically extract hierarchical features from input data through learning.

The structure of these models consists of several convolutional and pooling layers, which are then followed by the fully connected ones for classification purposes.

Training CNNs with a dataset of CAPTCHA images labeled with their corresponding labels helps the model recognize the patterns and shapes that distinguish different characters or objects in the CAPTCHA.

Using optimization methods such as gradient descent, the CNN adjusts its parameters to minimize the error classification, leading to high CAPTCHA recognition accuracy.

Recurrent Neural Networks (RNNs)

RNNs are another neural network class frequently used to solve tasks related to CAPTCHA breakages, especially where the CAPTCHA involves sequential information.

Unlike CNNs, which process input data in fixed-size windows, RNNs have recurrent connections within which they maintain internal states and process sequences of different lengths.

Using sequential input data (for example, pixel values from CAPTCHA images or one-hot encoded characters), the RNN learns to correlate dependencies and CAPTCHA patterns.

This enables RNN to scan properly CAPTCHAs with different lengths or complicated features of characters.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are machine learning models comprising two neural networks: a generator and a discriminator.

These networks are in a competitive learning process. The generator generates realistic data, while the discriminator distinguishes between real and fake data.

In the case of GAN bypassing CAPTCHA, such systems can be trained to create synthetic CAPTCHA images as close as possible to the real ones.

By progressively improving the generator output based on the response of the discriminator, GANs can generate realistic CAPTCHA images that make it possible for the CAPTCHA protection schemes to be easily bypassed.

Capsule Networks

Capsule Networks are a more recent neural network architecture that has shown promise in solving CAPTCHA challenges.

Capsule Networks are designed to overcome the limitations of CNNs in recognizing spatial hierarchies and relationships between different parts of an object.

The Capsule Networks can learn to detect the spatial relationships between different characters in the CAPTCHA, which is crucial for solving CAPTCHA challenges.

By training Capsule Networks with a dataset of labeled CAPTCHA images, the model can learn to recognize the spatial relationships between different characters and solve CAPTCHA challenges with high accuracy.

Challenges of Bypassing CAPTCHAs with Machine Learning and Ethical Considerations

Although machine learning techniques could open up a new sphere of CAPTCHA bypassing potential, they also raise ethical concerns and risks.

The misuse of CAPTCHA bypass techniques by fraudsters can enable malicious activities like spamming, credential stuffing, and denial-of-service attacks, which destroy the security and integrity of web platforms.

  1. Adversarial Attacks: CAPTCHA evasion techniques that use machine learning models are also susceptible to adversarial attacks where the evildoers create puzzles to deceive the model.

  2. Privacy Concerns: Bypass CAPTCHA techniques usually entail huge amounts of data collection and processing, which can lead to data privacy and data protection issues.

  3. Impact on Accessibility: Evading CAPTCHAs undermines the very purpose of providing online services that are friendly towards the disabled, who might be using CAPTCHA to prove their human identity.

  4. Legal and Regulatory Compliance: The potential threats of automated learning for circumventing CAPTCHA may be the source of legal issues and regulations, notably in countries with more strict privacy laws.


The CAPTCHA circumvention problem is complex and multifaceted, and it tends to evolve with the creation of new technologies.

While traditional approaches are still popular, the advent of machine learning provides the automatic ability to overcome CAPTCHA at scale.

Using more sophisticated algorithms and neural network architectures can enable programmers to develop complicated programs that can accurately solve captchas.

Still, bypassing techniques often outsmart security measures is strong evidence of the necessity of constant change and adjustment in the given area.

ScrapingAnt provides a CAPTCHA bypass capabilities, which in most cases can be used to avoid CAPTCHA challenges in web scraping. Still, in some cases, like when a particular website is in "Under Attack" mode, the CAPTCHA solving services is the only solution.

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