On the web, many websites and applications use Completely Automated Public Turing test to tell Computers and Humans Apart to prevent malicious operations from robots and automated programs. Verification code is a question raised through images or audio that requires the user to provide answers to prove that it is human. In this article, we will learn how to use Python to automatically identify digital verification codes so that they can automatically fill in or verify verification codes if needed.
1. Preparation
First, we need to install some Python libraries to process images and perform machine learning. We will use the Pillow library to process images, and the Scikit-learn library to implement machine learning models. Make sure you have installed these libraries:
pip install Pillow scikit-learn
2. Dataset
We need a dataset containing digital verification code images to train our model. You can find a dataset online or create your own. Make sure that the dataset contains enough image samples and that each image contains a clearly recognizable number.
3. Image preprocessing
Before training the model, we need to preprocess the image. This includes converting images to grayscale images, removing noise, and normalizing image size. Here is a simple image preprocessing function:
from PIL import Image
import numpy as np
def preprocess_image(image_path, target_size=(20, 20)):
image = (image_path).convert('L') # Convert to grayscale image image = (lambda x: 0 if x < 128 else 255) # Binary image = (target_size) # Resize image_array = (image) / 255.0 # Standardization return image_array.flatten()
4. Model training
We will use simple machine learning models such as support vector machines to train our verification code recognition system. First, we need to prepare the training data and train the model:
from sklearn import svm
import os
# Prepare training dataX_train = []
y_train = []
for filename in ('training_data'):
if ('.png'):
label = ('_')[0]
image_path = ('training_data', filename)
X_train.append(preprocess_image(image_path))
y_train.append(label)
# Train the modelclf = ()
(X_train, y_train)
5. Test the model
Once the model is trained, we can use test data to evaluate the performance of the model. For each test image, we preprocess it and use the trained model to predict it.
def predict_captcha(image_path):
preprocessed_image = preprocess_image(image_path)
predicted_digit = ([preprocessed_image])[0]
return predicted_digit
# Test the modeltest_image_path = 'test_data/test_captcha.png'
predicted_digit = predict_captcha(test_image_path)
print("Predicted Digit:", predicted_digit)
6. Application example
Verification code identification technology has a wide range of uses in practical applications. Here are some examples:
Automatic login and registration: Many websites require users to enter verification codes to verify their identity. Using verification code recognition technology, we can automatically fill in verification codes to achieve automatic login or registration function.
Data collection: When collecting network data, you sometimes need to access the target website through verification code. Verification code identification can help us automatically solve these verification codes, thereby achieving automated data collection.
Security testing: When conducting network security testing, verification code identification technology can be used to test whether the verification code system of the website is safe and reliable. By simulating an attack and trying to crack the verification code, the security of the website can be evaluated.
Anti-spam: Verification codes can be used to prevent automated programs from sending spam. Verification code identification technology can help email service providers filter out verification codes in spam, thereby improving the effectiveness of anti-spam.
7. Improve and optimize
Although the above example provides a basic verification code identification scheme, improvements and optimizations may be required in practical applications. Some improvements include:
Data enhancement: By transforming training data such as rotation, scaling, and translation, the diversity of data can be increased, thereby improving the generalization ability of the model.
Deep learning model: Using deep learning models (such as convolutional neural networks) can improve the accuracy of verification code recognition to a certain extent, especially when dealing with complex verification codes.
Model Integration: Integrating predictions from multiple different models can further improve identification accuracy, such as using methods such as voting or weighted averaging.
Real-time performance optimization: In practical applications, identification speed and resource consumption need to be considered. By optimizing models and algorithms, the recognition speed can be increased and the consumption of system resources can be reduced.
When we further think about the practical application of verification code recognition, we can consider the following scenario: a website requires users to fill in a verification code before logging in. We can write a Python script, use Selenium to automatically open the web page, intercept the verification code image, and identify the verification code through the previously trained model, and finally automatically fill in the verification code and complete the login operation.
Here is a simple example code:
from selenium import webdriver
from import Keys
import time
from PIL import Image
import numpy as np
from sklearn import svm
# Load the trained modelclf = ()
('captcha_model.pkl')
# Open the web pagedriver = ()
("/login")
# Intercept the verification code image and recognize itcaptcha_element = driver.find_element_by_xpath("//img[@id='captcha_image']")
captcha_element.screenshot('')
def preprocess_image(image_path, target_size=(20, 20)):
image = (image_path).convert('L')
image = (lambda x: 0 if x < 128 else 255)
image = (target_size)
image_array = (image) / 255.0
return image_array.flatten()
def predict_captcha(image_path):
preprocessed_image = preprocess_image(image_path)
predicted_digit = ([preprocessed_image])[0]
return predicted_digit
captcha_text = predict_captcha('')
# Enter the verification code and submit the formcaptcha_input = driver.find_element_by_xpath("//input[@id='captcha_input']")
captcha_input.send_keys(captcha_text)
username_input = driver.find_element_by_xpath("//input[@id='username']")
password_input = driver.find_element_by_xpath("//input[@id='password']")
username_input.send_keys("your_username")
password_input.send_keys("your_password")
login_button = driver.find_element_by_xpath("//button[@id='login_button']")
login_button.click()
(5) # Wait for the page to loadIn this example, we use the Selenium library to control the browser to perform automated operations, including opening a web page, finding verification code elements, intercepting verification code images, etc. Then, we use the previously trained model to identify the verification code image and obtain the verification code text. Finally, we automatically fill in the verification code and submit the login form.
This is just a simple example. In actual applications, more exception handling, verification code refresh mechanisms, etc. may need to be considered. But with this example, you can learn how to apply verification code recognition technology to actual automation tasks.
In the continuing example, we can add some additional features to improve the robustness and scalability of our code. These functions include error handling, verification code refresh and persistence model.
8. Error handling
In actual applications, various network problems, element positioning failures or verification code identification errors may be encountered. To increase the stability of the code, we can add appropriate error handling mechanisms, such as using the try-except block to catch exceptions and take corresponding measures.
try:
# Identify the verification code and fill in it captcha_text = predict_captcha('')
captcha_input = driver.find_element_by_xpath("//input[@id='captcha_input']")
captcha_input.send_keys(captcha_text)
except Exception as e:
print("Error:", e)
# Handle verification code recognition failure, such as reloading verification code picture or manually entering verification code9. Verification code refresh
Some websites may provide the function of refreshing verification codes. To cope with this situation, we can try to click the refresh button to obtain a new verification code image before identifying the verification code.
refresh_button = driver.find_element_by_xpath("//button[@id='refresh_button']")
refresh_button.click()
(1) # Wait for the new verification code to load10. Persistence Model
To avoid retraining the model every time the script is run, we can save the trained model to a file and load it if needed.
from joblib import dump, load
# Save the modeldump(clf, 'captcha_model.joblib')
# Loading the modelclf = load('captcha_model.joblib')
By adding the above features to our code, we can make the verification code identification script more robust and flexible, thus adapting to the handling of different websites and various exceptions.
In the continuing example, we can further consider optimizing the accuracy and stability of verification code recognition, as well as increasing the functionality of user interaction.
11. Verification code identification accuracy optimization
In order to further improve the accuracy of verification code recognition, you can try the following methods:
Model parameter adjustment: Adjust parameters of machine learning models such as support vector machines, such as C values and kernel functions, to optimize model performance.
Feature engineering: Perform more complex feature extraction on images, such as local Binary Patterns or feature pyramids, to increase the model's feature representation ability.
Data augmentation: Use image augmentation techniques (such as rotation, translation, scaling, inversion, etc.) to augment the training dataset to increase the robustness of the model.
12. User interaction function
In order to increase the functionality of user interaction, we can add some user interface elements, such as prompting the user to manually enter the verification code or selecting to click the refresh button.
manual_input = input("Enter the captcha text manually: ")
captcha_input = driver.find_element_by_xpath("//input[@id='captcha_input']")
captcha_input.send_keys(manual_input)
In this way, even if the verification code recognition fails, the user can still continue to operate by manually entering the verification code.
13. Automated login and error handling
Finally, we can integrate automated login and error handling code into one function for call in different scenarios.
def login(username, password):
try:
("/login")
#Other login steps... captcha_text = predict_captcha('')
captcha_input = driver.find_element_by_xpath("//input[@id='captcha_input']")
captcha_input.send_keys(captcha_text)
#Other steps to fill out the form... login_button = driver.find_element_by_xpath("//button[@id='login_button']")
login_button.click()
(5) # Wait for the page to load except Exception as e:
print("Login failed:", e)
# Handle login failure...
# Call login functionlogin("your_username", "your_password")Through the above improvements, we can make the verification code identification script more robust and flexible to adapt to different application scenarios and user needs. At the same time, these improvements also improve the maintainability and scalability of the code, making it easier to cope with future changes and needs.
Summarize
In this article, we explore how to use Python to automatically identify digital verification codes and apply them to real-life scenarios such as automated login to websites. We first introduce the concept of verification codes and why they are so important in network security and user verification. Then, we discuss the basic steps to implement verification code recognition using Python and some common libraries and tools such as Pillow, Scikit-learn, and Selenium.
We start with preprocessing verification code images and introduce how to convert images into grayscale images, binarization, resize, and standardize. Next, we discuss how to use machine learning models such as support vector machines to train and identify verification codes. We show how to prepare a training dataset, train a model, and evaluate model performance on a test dataset.
We then further discussed how to apply verification code recognition technology to actual scenarios, specifically automated login websites. We show how to use the Selenium library to control the browser for automated operations, including opening web pages, intercepting verification code images, identifying verification codes, and filling in forms.
Throughout the process, we emphasized the robustness and scalability of the code, and improved the stability and flexibility of the script by adding functions such as error handling, verification code refresh, persistence model and user interaction. Finally, we summarized some methods to further optimize the verification code identification system, including model parameter adjustment, feature engineering, and data enhancement.
Overall, this article provides a comprehensive guide to help readers understand how to use Python to automatically identify digital verification codes and apply them to real-world projects. Verification code recognition is a challenging but fun area. Through continuous learning and practice, we can continuously improve and optimize verification code recognition systems to provide more reliable and efficient solutions for network security and data automation.
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