Emerging Computer Applications

to Multidisciplinary Security Issues

Charles Tappert and Sung-Hyuk ChaSchool of Computer Science and Information

Systems

Previous Research Experience

Charles Tappert 26 years research at IBM

Speech recognition and processing Handwriting recognition and pen computing

7 years teaching at West Point Research on handheld/wearable computers

Sung Cha 3 years graduate work at the world renown Center for Excellence in Document Analysis

and Recognition (CEDAR), SUNY Buffalo Individuality of handwriting

2 years research at Samsung Medical information systems

Our Current Areas of Research

Related to Security

Handwriting and Forensic Document Analysis Speech/Voice Related Studies Individuality of Handwriting, Voice, Iris (fundamental studies for biometric

authentication) Related Pattern Recognition Research Wearable/Mobile/Pervasive Computing Research Forensics Applications

Security Related Research/Projects

D.P.S. Dissertations M.S. Dissertations Graduate and Undergraduate Students

Projects CS615-616 Software Engineering CS631 Computer Vision CS632 Pervasive Computing Research Seminar CS396 Pattern Recognition

Examples of Security-Related Research Studies Security-Related Research Publications NSF Funding Proposals

Security Related D.P.S. Dissertations

An Efficient First Pass of a Two-Stage Approach for Automatic Language Identification of Telephone Speech, Jonathan Law (2002)

Information Assurance Strategic Planning: A Taxonomy, Steven Parshley (2004)

A Cybercrime Taxonomy, Vincent Gisonti (2004)

Real-time Trifocal Vision with Locate Positioning System, Yi Rong (2004)

Stego-Marking in TCP/IP Packets, Eric Cole (2004)

The Computer Forensics and Cybersecurity Governance Model, Kenneth Brancik (2005)

Security Related M.S. Dissertations

Forged Handwriting Detection, Hung-Chun Chen (spring 2003) Speaker Individuality, Naresh Trilok (fall 2003) More coming

Security Related Projects

Handwriting Forgery Detection, Forgery Quiz System Recognizing a Handwriter’s Style/Nationality Emergency Pre-Hospital Care Communication System Eigenface Recognition System Interactive Visual Systems (collab. with RPI, NSF funding?) Object Tracking System (Surveillance) Object Segmentation (X-ray scan) Biometric Authentication (Fingerprint, Iris, Handwriting,

Voice) Others: Steganography, Wireless Security, Forensics, Spam

Detection, Language Classification from Text

Project Customers/Sources

Pace University School of Computer Science and Information Systems Dyson College of Arts and Sciences Lubin School of Business Lienhard School of Nursing Department of Information Technology Doctor of Professional Studies in Computing Program Office of Planning, Assessment, Research, and

Academic Support Outside Organizations

Northern Westchester Hospital Columbia Presbyterian Medical Center Psychology Department at SUNY New Paltz Yonsei University, Korea CEDAR, SUNY Buffalo Rensselaer Polytechnic Institute IBM T.J. Watson Research Center

Benefits of Student Projects

Stellar real-world learning experience for students

Customers receive valuable systems Promotes interdisciplinary collaboration and

Pace and local community involvement Furthers student and faculty research Enhances relationships between the university

and local technology companies Increases national recognition of the university

Examples of Security-Related Research

Studies

Forgery Detection Interactive Visual System Speaker Individuality

Forgery Detection: Key Idea

Forensic literature indicates that successful forgers often forge handwriting shape and size by carefully copying or tracing the authentic handwriting

Exploit computing technology to investigate this and possibly to develop techniques to aid forensic document examiners

Forgery Detection: Hypotheses

Good forgeries – those that retain the shape and size of authentic writing – tend to be written more slowly (carefully) than authentic writing

Good forgeries are likely to be wrinklier (less smooth) than authentic handwriting

Forgery Detection: Methodology

Sample collection: online, scan to get offlineFeature extraction: Speed, WrinklinessStatistical analysis

(b) Number of in the boundary = 32

(a) Number of in the boundary = 69

(b) Number of in the boundary = 32

(a) (b)

)2log(/.___

.___log

reslowinboundary

reshighinboundarysWrinklines

1085.1)2log(/)32

69log( sWrinklines

Fractal Measure of Wrinkliness

Forgery Detection: Experiment

10 subjects, each wrote 3 authentic handwriting samples 3 forgeries of each of the other 9 subjects

30 authentic and 270 forged samples Significance results (T-test)

Forgeries are written slower: p = 5.90E-09 Forgeries are wrinklier: p = 0.0205

Interactive Visual System (IVS)

IVS is a technology, not just a flower identification application

We also have preliminary results on flag recognition, and we plan to explore the applications of sign, face, and skin-lesion recognition

IVS Motivation

Image recognition can be a difficult problem Modern AI and pattern recognition techniques

try to automate the process – that is, they do not include the human in the equation

Humans and computers have different strengths Computers excel at large memory and computation Humans excel at segmentation

We propose combining human and computer to increase the speed and accuracy of recognition

IVS Flower User Interface

Load Flower Image Select Features Identify Previous 3 Hits Next 3 Hits Store New Flower Auto Feature

Extract List Extracted

Features

IVS Flower Shape Model

IVS: Flag Recognition

We have extended the Interactive Visual System to other applications, and have preliminary results on flag recognition

Demonstration by Dr. Sung Cha

IVS: NSF Proposal Applications

Foreign Sign Recognition Shape model: rectangle

Face Recognition Shape model: 3D face

template

Skin Lesion Recognition Shape model?

Speaker Individuality

Hypothesis: a person’s voice is unique and therefore we can verify the identity of an individual from his/her voice samples

Methodology: use a statistically inferable dichotomy (verification) model that Dr. Cha has used to show handwriting individuality

Speaker Individuality: Methodology

Segment common portion of utterance: “My name is”

Compute spectral data: output from 13 filters every 10 msec

Extract fixed number of features per utterance from the spectral data

Use the dichotomy (verification) model to obtain experimental results

Speaker Individuality: Segmentation

“My name is” from Two Speakers

Neural Network Dichotomy Model

Same/Different

),...,,( 21xd

xx fff

),...,,( 21yd

yy fff

),( 11yx ff

),( 22yx ff

),( yd

xd ff

Feature Extrac-

tion

Distancecompu-tation

Speaker Individuality: Experiments

10 samples from each of 10 speakers 450 intra-speaker distances 4500 inter-speaker distances Train NN on a subset of the intra-

speaker and inter-speaker distances Test on different subsets 94 percent accuracy 98 percent with bad samples

removed

Security-Related Research Publications

http://csis.pace.edu/csis/cgi-front/sec/security.pl?cat=11

Security Related Funding Proposals

NSF 01-100, CISE-HCI Interactive Visual Processing Collaboration with RPI Submitted January 8, 2004

NSF 03-602 Computer Vision Individuality Studies (fundamental

studies for biometric authentication) Submitted December 19, 2003