emerging computer applications to multidisciplinary security issues
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Emerging Computer Applications to Multidisciplinary Security Issues. Charles Tappert and Sung-Hyuk Cha School of Computer Science and Information Systems. Previous Research Experience. Charles Tappert 26 years research at IBM Speech recognition and processing - PowerPoint PPT PresentationTRANSCRIPT
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