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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

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emerging computer applications to multidisciplinary security issues

Emerging Computer Applications to Multidisciplinary Security Issues

Charles Tappert and Sung-Hyuk Cha

School of Computer Science and Information Systems

previous research experience
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
Our Current Areas of ResearchRelated 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
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
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
Security Related M.S. Dissertations
  • Forged Handwriting Detection,

Hung-Chun Chen (spring 2003)

  • Speaker Individuality,

Naresh Trilok (fall 2003)

  • More coming
security related projects
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
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
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
Examples of Security-Related Research Studies
  • Forgery Detection
  • Interactive Visual System
  • Speaker Individuality
forgery detection key idea
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
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
Forgery Detection: Methodology
  • Sample collection: online, scan to get offline
  • Feature extraction: Speed, Wrinkliness
  • Statistical analysis



(a) Number of in the boundary = 69

(b) Number of in the boundary = 32

(b) Number of in the boundary = 32

forgery detection experiment
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

  • This presentation will probably involve audience discussion, which will create action items. Use PowerPoint to keep track of these action items during your presentation
  • In Slide Show, click on the right mouse button
  • Select “Meeting Minder”
  • Select the “Action Items” tab
  • Type in action items as they come up
  • Click OK to dismiss this box
  • This will automatically create an Action Item slide at the end of your presentation with your points entered.
Interactive Visual System (IVS)
ivs motivation
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
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 flag recognition
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
IVS: NSF Proposal Applications
  • Foreign Sign Recognition
    • Shape model: rectangle
  • Face Recognition
    • Shape model: 3D face template
  • Skin Lesion Recognition
    • Shape model?
speaker individuality
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
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
neural network dichotomy model
Neural Network Dichotomy Model









speaker individuality experiments
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
Security-Related Research Publications
  • http://csis.pace.edu/csis/cgi-front/sec/security.pl?cat=11
security related funding proposals
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