1. CAPTCHA solving Tianhui Cai Period 3

2. CAPTCHAs Completely Automated Public Turing tests to tell Computers and Humans Apart User is human or machine? Prevents spam on 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)‏ Generate training/testing data set Letter identification (next section)‏

5. Procedure – cont’d Train on image data Test Review/Analyze

6. Implementation JAVA / ruby Acquire images – captchas.net  formula to get actual text from image Remove background clutter – median filter, etc Segmentation – flood fill Letter identification – neural network

7. First quarter Three layer backpropagation neural network Neural network – good for classification  Used often for image recognition Artificial neurons  convert input to output Backpropagation  used to let the neural network learn

8. Second quarter Image processing – Java ImageIO Noise removal Segmentation

9. Third quarter Neural network – made save / load  saved into a text file  a neural network can be trained multiple times Downloaded necessary images (ruby)‏  captchas.net  filename is what the image says Analyzed image outputs from images Cropped and centered segmented letters  uniform letter size  centered around bounding box  uniformity is good for training

10. Fourth quarter Train and test Problem: it didn’t work  resized images to 11x9, and then it worked Gather data / analyze  learning rate  number of training iterations  Best around 92% success rate (when training data and testing data are separate)‏  Higher when testing data is part of training data

11. Future? Segment letters that stick together (flood fill won’t work there)‏ using vertical divides Shape context Conquer more complicated distortions and noise Reduce blobbyness / better noise removal Neural network optimizations (structure, number of nodes)‏