Emerging Computer Applications to Multidisciplinary Security Issues

1. Emerging Computer Applications to Multidisciplinary Security Issues Charles Tappert and Sung-Hyuk Cha School of Computer Science and Information Systems

2. 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

3. 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

4. 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

5. 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)

6. Security Related M.S. Dissertations • Forged Handwriting Detection, Hung-Chun Chen (spring 2003) • Speaker Individuality, Naresh Trilok (fall 2003) • More coming

7. 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

8. 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

9. 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

10. Examples of Security-Related Research Studies • Forgery Detection • Interactive Visual System • Speaker Individuality

11. 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

12. 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

13. Forgery Detection: Methodology • Sample collection: online, scan to get offline • Feature extraction: Speed, Wrinkliness • Statistical analysis

14. (b) (a) (a) Number of in the boundary = 69 (b) Number of in the boundary = 32 (b) Number of in the boundary = 32

15. Fractal Measure of Wrinkliness

16. 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

17. 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)

18. 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

19. 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

20. IVS Flower Shape Model

21. 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

22. IVS: NSF Proposal Applications • Foreign Sign Recognition • Shape model: rectangle • Face Recognition • Shape model: 3D face template • Skin Lesion Recognition • Shape model?

23. 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

24. 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

25. Speaker Individuality: Segmentation

26. “My name is” from Two Speakers

27. Neural Network Dichotomy Model Feature Extrac- tion Distance compu- tation Same/ Different

28. 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

29. Security-Related Research Publications • http://csis.pace.edu/csis/cgi-front/sec/security.pl?cat=11

30. 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