kloover.com

On September 26th, 2008 I will be presenting some of my work on Video CAPTCHAs at the IEEE Western New York Image Processing Workshop 2008 in Rochester, NY. The workshop will be held in the Imaging Science building at RIT (registration details can be found at the above link). The paper is in the form of a 4 page "extended abstract" and can be downloaded below.

Paper

The paper can be downloaded here.

Bibtex Entry

@inproceedings{videoCAPTCHAsUsabilityVsSecurity,
	Title = {Video CAPTCHAs: Usability vs. Security},
	Author = {Kurt Alfred Kluever and Richard Zanibbi},
	Booktitle = {Proceedings of the IEEE Western New York Image Processing Workshop 2008},
	Address = {Rochester, NY, USA},
	Month = {September},
	Year = {2008}
}

The PayPal.com CAPTCHA suffers several weaknesses: fixed font face, fixed font size, no distortions, trivial background noise, and it’s easy to segment. In this experiment, a three step algorithm has been developed to break the PayPal CAPTCHA. The image is preprocessed to remove noise using thresholding and a simple cleaning technique, and then segmented using vertical projections and candidate split positions. Four classification methods have been implemented: pixel counting, vertical projections, horizontal projections and template correlations. The system was trained on a sample of twenty PayPal CAPTCHAs to create thirty-six training templates (one for each character: 0-9 and A-Z). A separate sample of 100 PayPal CAPTCHAs were used for testing. The following success rates have been achieved using the different classifiers: 8% pixel counting, vertical projections 97%, horizontal projections 100%, template correlations 100%. Three of the trained classifiers out perform the 88% success rate of Pwntcha.

Example

Preprocess

1. Original:
2. Grey Scale:
3. Thresholding:
4. Further Cleaning:

Segment

1. Segmented:
   
2. Padded:
   

Classify

• Pixel Counting: 8% Break Rate
• Vertical Projections: 97% Break Rate
• Horizontal Projections: 100% Break Rate
• Template Correlations: 100% Break Rate

Paper

The final paper including MATLAB source code, sample runs, and results can be downloaded here.

Presentation

A copy of the slides used for a presentation of this experiment can be downloaded here.

Data

The 20 training and 100 testing PayPal CAPTCHA images are available to download here.

Source Code

Complete MATLAB code (281 lines, well commented) for preprocessing, segmenting, and classifying the images is available here.

For my independent study, I investigated optical character recognition techniques and their application to recognizing text-based HIPs (methods used to distinguish human users and machines on the internet). This study is an extension of methods covered in neural networks and machine learning, computer vision, and artificial intelligence. The report includes experimental results of breaking the ASP Security Image Generator (CAPTCHA) v2.0 with a 72% success rate. Posting of source code is not currently planned. However, my paper contains fairly detailed steps and can be downloaded here.

As part of my Artifical Intelligence course, we developed a rule-based expert system that can autonomously govern a building’s environment to optimize user comfort and energy consumption, whilst providing safety and monitoring functions. The expert system has been developed using the Java programming language and the Java Expert System Shell (JESS). Rules are stored as an external resource and can be modified in real time without requiring a rebuild of the entire project. Write-up 1 includes problem description, design considerations, and implementation details. Write-up 2 includes testing results and a comparison to another system.

Optical character recognition refers to the process of translating images of hand-written, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size. The OCR process is complicated by noisy inputs, image distortion, and differences between typefaces, sizes, and fonts. Artificial neural networks are commonly used to perform character recognition due to their high noise tolerance. In my Artificial Intelligence course, I explored several OCR techniques which utilize ANN’s.

Paper

My final writeup where I surveyed four OCR techniques which utilized ANNs can be downloaded here.

Presentation

I also gave a final presentation on my research where I compared and contrasted four methods. My slides can be downloaded here.

For my Computer Vision course project, I implemented the seam carving technique by Shai Avidan of Mitsubishi Electronic Research Labs and Ariel Shamir of The Interdisciplinary Center and MERL. My final paper, presentation, and code for my seam carving project is now available.

Paper

My final writeup can be downloaded here.

Presentation

Presentation available here!

Code

Java code available here!
Matlab code available here!

Video

Examples

Original image

Image with 100 lowest energy seams shown

Image with 100 seams removed (no noticeable artifacts)

Image with 250 seams removed (artifacts start to appear)

The IEEE paper (Improving Measurement Accuracy in Sensor Networks by an Object Model Generation and Application) which I co-authored with Dr. Leon Reznik is now available on IEEE’s website. It was prepared for the IEEE Sensors 2007 Conference in Atlanta, GA. Bibtex entry:

@inproceedings{4388413,
	Address = {Atlanta, GA},
	Author = {Reznik, Leonid and Kluever, Kurt Alfred},
	Booktitle = {Proceedings of the 6th Annual IEEE Conference on Sensors},
	Doi = {10.1109/ICSENS.2007.4388413},
	Issn = {1930-0395},
	Month = {October},
	Pages = {371-374},
	Publisher = {IEEE Computer Society},
	Title = {Improving Measurement Accuracy in Sensor Networks by an Object Model Generation and Application},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/ICSENS.2007.4388413}
}

I wrote a paper on a sample of current methods of biometric feature extraction for my Intelligent Secure Systems course taught by Dr. Leon Reznik. The paper can be downloaded here.

My team designed the questions, conducted an online survey, and analyzed the results from approximately 1000 respondents. The respondents were questioned about their computer security practices. The survey was conducted as part of my Security Measurement and Testing course taught by Dr. Leon Reznik.  An informal publication was made in RIT’s ITS monthly newsletter.

This is one of my first papers on CAPTCHAs which I wrote for my Privacy and Security course taught by Warren R. Carithers. The survey paper can be downloaded here.