CAPTCHA Processing

CPRE 583 Fall 2010 Project

CAPTCHA Processing Responsibilities

• Brian Washburn – Loading Image into RAM and Preprocessing and related portion of writeup/presentation • Nicholas Rundle – Text Detection, related portion of writeup/presentation, and writeup/presentation Assembly • Daniel Uhrman – Text Recognition and related portion of writeup/presentation

CAPTCHA Processing Motivation

The ever increasing spam e-mail has led to the development of CAPTCHAs to try and distinguish between humans and computers. The ability to distinguish between humans and computers is becoming more difficult as computer systems improve. New CAPTCHA systems that are harder to break with a computer are necessary in order to maintain security. This project aims to break current CAPTCHA systems as a means of showing the weaknesses inherent in the system and to motivate ways to improve upon the current designs.

CAPTCHA Processing Design

Image FPGA Text o v e r l o o k s

Interface Design

There are two main interfaces into the system: 1.) Ethernet to/from the PPC 2.) Loads and Stores to/from PPC and APU Terminal Ethernet PowerPC 440 Load Store Auxiliary Processor Unit

Client

File Transfer Protocol

Server TCP Connect “220” “AUTH” “234” “USER Captcha Group” “230”

Client

Passive FTP

Server Control Port PASV 227 IP, IP, IP, IP, Port, Port Connect to Addr Server Data Port ACK

DATA

Terminate “226 Success”

Features of the Xilinx llwip4 library

(Lightweight IP) • Standard Berkeley model for sockets – Lwip_listen() – Lwip_write() – Lwip_socket() – Lwip_bind() – Lwip_socket() (SOCK_STREAM for TCP) – Lwip_accept() – Read() – Close()

lxilKernel library

• Features an easy threading model • Pthread like mutex’ing FTP Server Thread Process Control Port Process Data Port Control Port listen Thread Listen Data Port

Captcha Controller

• Our Controller coordinates dataflow between all of our different subsystems Auxiliary Processor Unit Segmenter Classifier BRAM BRAM BRAM

Future PPC Work

• The PowerPC can be used for pre processing – Noise Reduction – Edge detection – Color correction • Also, it could be used to parse the headers of image files and pass this data along coherently

Segmenter Unit

• Searches columns of the input image for the edges of letters and copies these columns into BRAM.

• For uniformity, output letters are fixed size of 32x32. Right filled with white pixels.

Segmenter Unit

Input bram address 0 Output bram address 0 Address 32

Segmentation

• Histogram thresholding • Edge detection • Region-based

Classifier Unit

• Receives indication of successful segmentation of up to 8 characters from Segmenter • Reads Segmented Characters from BRAM.

• Compares each input character to 36 template characters (A-Z and 0-9).

• Outputs an array of up to 8 ASCII values.

Horizontal Projection

• The segmented characters and template characters are analyzed using HP (horizontal projection).

• The HP is determined by calculating the sum of each horizontal row of pixel values for an image.

• For our 32x32 pixel images, the HP values will be arrays of size 32 containing sums of up to 32 in each position.

Classifier Template BRAM

• The expected HP values are pre-calculated for each template character.

• These values are stored in a ROM made in a BRAM IP core that is preconfigured with a .COE file.

• The input images from the segmenter are read from BRAM and compared to each of the template characters to find the best match.

Correlation Algorithm

• The HP values are compared utilizing the correlation function from statistics shown below: • Where: X and Y are the HP values for an input image and a given template and N is the length of the HP array.

Correlation Algorithm Cont’d

• Due to the following constraints we went with the following modification of the correlation equation: – No IP Core for floating point conversion in version 10.1 of tools.

– No IP Core for an integer-based square root function.

– Potential overflows as a result of large summations and multiplication.

• Implemented as 16 dedicated multipliers, 1 larger width multiplier as well as 1 dedicated divider.

Potential Future Work

• Implement “learning” functionality in classifier so that the template ROM is actually a RAM and can be updated based upon CAPTCHA techniques it observes.

• Utilize CAPTCHA Detection Unit for name recognition from security badges, or license plate identification on speed cameras.

Integration

• In its current form, the project works fully in Modelsim with various test inputs.

• In HW, the project works all the way up to the classifier. The classifier unit has many multipliers and uses a pipelined divider which is a potential point of timing irregularities. We are adding pipeline stages to account for these timing issues.

Potential Future Work

• Implement “learning” functionality in classifier so that the template ROM is actually a RAM and can be updated based upon CAPTCHA techniques it observes.

• Utilize CAPTCHA Detection Unit for name recognition from security badges, or license plate identification on speed cameras.

CAPTCHA Processing Papers

• Algorithm to Break Visual CAPTCHA (ICETET 2009) • Bio-inspired unified model of visual segmentation system for CAPTCHA character recognition (SiPS 2008) • CAPTCHA Security: A Case Study (Security & Privacy July 2009) • Recognizing object in adversarial clutter: breaking a visual CAPTCHA (Computer Vision and Pattern Recognition 2003) • Reverse Engineering CAPTCHAs (WCRE 2008)