ML-based CAPTCHA Solver – IT and Computer Engineering Guide
1. Project Overview
Objective: Develop a machine learning model, particularly a
Convolutional Neural Network (CNN), to solve image-based CAPTCHAs.
Scope: Understand the application of deep learning in solving CAPTCHA
challenges commonly used for security purposes.
2. Prerequisites
Knowledge: Basics of neural networks, convolutional
operations, image preprocessing, and Python programming.
Tools: Python, TensorFlow/Keras or PyTorch, OpenCV, NumPy, and Matplotlib.
Data: A dataset of image-based CAPTCHAs with corresponding labels.
3. Project Workflow
- Data Collection: Collect or generate a dataset of CAPTCHAs with labels.
- Data Preprocessing: Resize, normalize, and binarize CAPTCHA images.
- Model Architecture: Design a CNN suitable for image classification tasks.
- Model Training: Train the model on labeled CAPTCHA images.
- Evaluation: Assess performance using metrics like accuracy and loss.
- Deployment: Integrate the trained model into a script or application for real-time CAPTCHA solving.
4. Technical Implementation
Step 1: Import Libraries
import numpy as np
import cv2
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
Step 2: Data Preprocessing
# Load CAPTCHA dataset
images = [] # List to store images
labels = [] # List to store
corresponding labels
# Example: Load image and label from dataset
for image_path, label in dataset:
img = cv2.imread(image_path,
cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (100, 50)) # Resize to standard dimensions
images.append(img)
labels.append(label)
# Convert to numpy arrays and normalize
X = np.array(images).reshape(-1, 50, 100, 1) / 255.0 # Normalize
y = to_categorical(np.array(labels)) #
One-hot encode labels
Step 3: Train-Test Split
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
Step 4: Define CNN Model
model = Sequential([
Conv2D(32, (3, 3), activation='relu',
input_shape=(50, 100, 1)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3),
activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dense(y.shape[1],
activation='softmax') # Output layer
matches number of classes
])
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
Step 5: Train the Model
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test,
y_test), batch_size=32)
Step 6: Evaluate and Test
# Evaluate model
loss, accuracy = model.evaluate(X_test, y_test)
print(f'Accuracy: {accuracy * 100:.2f}%')
# Test on a new CAPTCHA
test_img = cv2.imread('new_captcha.png', cv2.IMREAD_GRAYSCALE)
test_img = cv2.resize(test_img, (100, 50)).reshape(1, 50, 100, 1) / 255.0
prediction = model.predict(test_img)
print(f'Predicted label: {np.argmax(prediction)}')
5. Results and Insights
Evaluate the model's accuracy on the test dataset and analyze incorrect predictions to identify areas of improvement.
6. Challenges and Mitigation
Dataset Quality: Ensure a diverse dataset representing
different CAPTCHA styles.
Complexity: Handle multi-character CAPTCHAs using segmentation or recurrent
networks.
7. Future Enhancements
Expand the model to handle CAPTCHAs with multiple
characters.
Incorporate adversarial training to improve robustness.
8. Conclusion
The ML-based CAPTCHA Solver project demonstrates the power of convolutional neural networks in solving security challenges while showcasing the practical application of deep learning.