1. Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #15 Secure Multimedia Data Management and Data Mining March 13, 2006

2. Objective l This unit provides an overview of multimedia information management including multimedia data management and multimedia data mining. Security issues will also be discussed l Reference: Managing and Mining Multimedia Databases, CRC Press, Thuraisingham, June 2001

3. Outline l Multimedia Data Management Systems - Architecture - Modeling - Functions l Security l Developments and Challenges l Multimedia Mining l Future Directions

4. Sources of Multimedia Data Text, Video, Audio, Imagery

5. Why Multimedia Database Management System? l Need persistent storage for managing large quantities of multimedia data l A Multimedia DBMS manages multimedia data such as text, images, audio, animation, video l Extended by a Browser to produce a Hypermedia DBMS l Heterogeneity with respect to data types l Numerous Applications - Entertainment, Defense and Intelligence, Telecommunications, Finance, Medical

6. Architectures: Loose Integration Multimedia File Manager Metadata Module for Integrating Data Manager with File Manager User Interface Data Manager for Metadata Multimedia Files

7. Architectures: Tight Integration User Interface MM-DBMS: Integrated data manager and file manager MM-DBMS: Integrated data manager and file manager Multimedia Database

8. Architectures: Functional User Interface Multimedia Database Representation Distribution Quality of Service Real-time Heterogeneity Query/Update Transactions Metadata Integrity/Security Storage

9. Data Model: Scenario Example: Object representation Object A 2000 Frames 4/95 8/95 5/95 10/95 Object B 3000 Frames

10. Data Model: Object Object A Object A ID 2098 interval (4/95, 8/95) contents Frames 2000

11. Data Model: Object-Relational IDIntervalContentsFrame 2098(4/95, 8/95)2000

12. Functions: Editing Example: Object editing Editing objects A and B by merging them to form a new object over interval 4/15/95 to 8/15/95 4/15/958/15/95 Object C

13. Multimedia Data Access: Some approaches l Text data - Selection with index features - Methods: Full text scanning, Inverted files, Document clustering l Audio/Speech data - Pattern matching algorithms l Matching index features given for searching and ones available in the database l Image data - Identifying geometric boundaries, Identifying spatial relationships, Image clustering l Video data - Retrieval with metadata, Pattern matching with images

14. Metadata for Multimedia l Metadata may be annotations and stored in relations - I.e., Metadata from text, images, audio and video are extracted as stored as text - Text metadata may be converted to relations by tagging and extracting concepts l Metadata may be images of video data - E.g., certain frames may be captured as metadata l Multimedia data understanding - Extracting metadata from the multimedia data

15. Storage Methods l Single disk storage - Objects belonging to different media types in same disk l Multiple disk storage - Objects distributed across disks l Example: individual media types stored in different disks l I.e., audio in one disk and video in another l Need to synchronize for presentation (real-time techniques) l Multiple disks with striping - Distribute placement of media objects in different disks l Called disk striping

16. Security Issues l Access Control l Multilevel Security l Architecture l Secure Geospatial Information Systems

17. Access Control for Multimedia Databases l Access Control for Text, Images, Audio and Video l Granularity of Protection - Text l John has access to Chapters 1 and 2 but not to 3 and 4 - Images l John has access to portions of the image l Access control for pixels? - Video and Audio l John has access to Frames 1000 to 2000 l Jane has access only to scenes in US - Security constraints l Association based constraints E.g., collections of images are classified

18. MLS Security

19. Example Security Architecture: Integrity Lock

20. Inference Control

21. Authorization Model for Secure Geospatial Systems l Geospatial images could be Digital Raster Images that store images as pixels or Digital Vector Images that store images as points, lines and polygons l GSAM: Geospatial Authorization Model specifies subjects, credentials, objects (e.g, points, lines, pixels etc.) and the access that subjects have to objects l Reference: Authorization Model for Geospatial Data; Atluri and Chun, IEEE Transactions on Dependable and Secure Computing, Volume 1, #4, October – December 2004.

22. Secure Geospatial Systems

23. Directions and Challenges in Managing Multimedia Databases l Much work on data models, query languages, architectures and indexing (still need more work on indexing) l Increasing interest in - Quality of Service for Multimedia Data Management l Synchronizing audio and video l Synchronizing storage retrieval and presentations l Real-time scheduling techniques - Distributed multimedia database management l Query processing techniques - Multimedia on the Web l Capture, annotate, summarize, disseminate - Mining multimedia data l Extracting information previously unknown

24. Example: Automated Digital Capture, Analysis and Publication of Broadcast News Video Source Scene Change Detection Speaker Change Detection Silence Detection Commercial Detection Key Frame Selection Story Segmentation Named Entity Tagging Broadcast News Editor (BNE) Broadcast News Navigator (BNN) Video and Metadata Multimedia Database Management System Web-based Search/Browse by Program, Person, Location,... Imagery Audio Closed Caption Text Segregate Video Streams Analyze and Store Video and Metadata Story GIST Theme Frame Classifier Closed Caption Preprocess Correlation Token Detection Broadcast Detection

25. Example Web Page Select Story

26. Elaborate on Story Key Frame Source Closed Caption Video 6 Most Frequent Tags Summary Related Web Sites

27. Apply QueryFlocks Data Mining Tool: (MITRE/Stanford Tool)

28. Extracting Relations from Text for Mining: An Example Text Corpus Repository Concept Extraction Association Rule Product Goal: Find Cooperating/ Combating Leaders in a territory

29. Image Processing: Example: Change Detection: l Trained Neural Network to predict “new” pixel from “old” pixel - Neural Networks good for multidimensional continuous data - Multiple nets gives range of “expected values” l Identified pixels where actual value substantially outside range of expected values - Anomaly if three or more bands (of seven) out of range l Identified groups of anomalous pixels

30. In Conclusion: l Multimedia data management is getting mature l Numerous applications in several industries l Challenge is to mine multimedia databases l Work is just beginning on multimedia data mining l Web provides lots of opportunities and challenges for multimedia data management l We cannot forget about security and privacy