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A Machine Learning Based Captcha Cracking Project
Citation preview
CAPTCHA Cracking
Knowledge-Base Intelligent
System
Omer Shafiq FA09-BCS-098Ihsan Ullah FA09-BCS-153Adnan Bajwa FA09-BCS-163
Brief Description
• Captcha Cracking System cracks the captcha images intelligently and then make the knowledge-base of the policy of cracking captcha images and reflexly learn this knowledge to crack the another captcha images
• Enables the system to implement the online learning through which we can achieve the optimal solution
• Our System depends on multiple phases, which are explained below that how each of them works and integrate to make this System.
The Process
• The Learning Process Takes Place After Creating Instances List From Filtered Data
Analysis• CAPTCHA IMAGE: Our System will be working on
CAPTCHA breaking written in Java using some external OCR libraries and some Machine Learning Libraries.
• DE-NOISE: For the first section, de-noising, we will have to find a smart way to de-noise our input CAPTCHA via some image de-noise algorithm for our approach.
Analysis• SEMENTATION: For the segmentation stage, we need
to split the image of string in characters via different segmentation algorithms.
• BINARY BIT STREAM: Segmentation gives us the different segmented images.
Analysis
• DATASET: Data Set contains the instances includes the feature vectors and desired target output value which will be predict through applying desired Classifier.
Architecture
Intelligence Aspect
• Project Intelligently Recognizes The Pattern of the image to classify
• Project can simultaneously denoise and segment captchas parallel
• Classification depend upon the model you have trained
Application Screenshots
• CAPTCHA CRAWLER Crawels as many captchas as we want from captchas.net server created on C#.NET Framework 4
Application Screenshots
Application Screenshots
Application Screenshots
Results and conclusion Classifier: Decision Tree (J48)Instances: 353Attributes: 191
Test mode: 10-fold cross-validation
Correctly Classified Instances 168 47.7273 %Incorrectly Classified Instances 184 52.2727 %Kappa statistic 0.4519Mean absolute error 0.04 Root mean squared error 0.183 Relative absolute error 54.4013 %Root relative squared error 95.4941 %
Results and conclusion Classifier: Artificial Neural-Net (MultiLayer-Preceptron)Instances: 353Attributes: 191
Test mode: 10-fold cross-validation
Correctly Classified Instances 295 83.8068 %Incorrectly Classified Instances 57 16.1932 %Kappa statistic 0.8301Mean absolute error 0.0171Root mean squared error 0.0966Relative absolute error 23.2233 %Root relative squared error 50.4266 %
Results and conclusion Classifier: Support Vector Machine(SVM)Instances: 353Attributes: 191
Test mode: 10-fold cross-validation
Correctly Classified Instances 304 86.3636 %Incorrectly Classified Instances 48 13.6364 %Kappa statistic 0.8569Mean absolute error 0.0711Root mean squared error 0.1861Relative absolute error 96.8087 %Root relative squared error 97.1338 %
Results and conclusion Classifier: Naive Bayes (NaiveBayesin)Instances: 353Attributes: 191
Test mode: 10-fold cross-validation
Correctly Classified Instances 268 76.1364 %Incorrectly Classified Instances 84 23.8636 %Kappa statistic 0.7499Mean absolute error 0.018 Root mean squared error 0.1282Relative absolute error 24.5384 %Root relative squared error 66.9059 %
Results and conclusion
Decision Tree SVM ANN NaïveBayes0
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Correct ClassificationMissclassification
Visual Results and Conclusion