emerging computer applications to multidisciplinary security issues l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Emerging Computer Applications to Multidisciplinary Security Issues PowerPoint Presentation
Download Presentation
Emerging Computer Applications to Multidisciplinary Security Issues

Loading in 2 Seconds...

play fullscreen
1 / 30

Emerging Computer Applications to Multidisciplinary Security Issues - PowerPoint PPT Presentation


  • 478 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Emerging Computer Applications to Multidisciplinary Security Issues' - richard_edik


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
slide14

(b)

(a)

(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

Feature

Extrac-

tion

Distance

compu-

tation

Same/

Different

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