CAPTCHA solving

1
CAPTCHA solving

• Tianhui Cai
• Period 3

2
CAPTCHAs

• Completely Automated Public Turing tests to tell
  Computers and Humans Apart
• Determines whether a user is a human or a
  computer to prevent spam, etc
• Found on lots of website registration pages
• Audio and visual
• Visual contains noise, distortions
• rotation
• translation
• scaling
• noise
• warp

3
Goal

• Solve a CAPTCHA, pretend to be a human
• Read the image figure out what it says
• This has been done before.
• Show weaknesses of visual CAPTCHAs

4
Procedure?

• Acquire image (from internet)?
• Remove background clutter
• Segmentation (separating letters)?
• Letter identification

5
Implementation

• JAVA
• Acquire images captchas.net
• formula to get actual text from image
• Remove background clutter median filter, etc
• Segmentation flood fill
• Letter identification neural network

6
First quarter progress summary

• Three layer backpropagation neural network
  written and tested
• It works
• Neural network good for classification.
• Used often for image recognition
• Consists of artificial neurons, which convert
  input to output
• Backpropagation is used to let the neural network
  learn
• Training
• Testing

7
Second quarter process

• Image processing
• Noise removal
• Segmentation

8
Noise removal

• Modified median filter
• Advantages unlike Gaussian blur, it doesn't lose
  edge data
• Disadvantages It compromises edge integrity and
  noise

9
Segmentation

• Flood fill
• Advantages It's easy and often used
• Disadvantages letters may be stuck together in
  some cases and broken up in others

10
Third quarter goals

• Neural network make it able to be saved so that
  it can be trained
• Feed inputs from flood fill into neural net for
  training, then test neural net and run