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CS919 Multimedia Forensics (not taught in 2016/17)

CS919 15 CATS (7.5 ECTS) Term 2


Option - MSc Computer Science, MSc Data Analytics


Academic Aims

The aim is for the students to become familiar with multimedia-based digital forensics that is useful in identifying source devices of digital content, content integrity verification, copyright protection, steganography, steganalysis, and content authentication. It is intended for the students to acquire the state-of-the-art multimedia-based digital forensic techniques that are in acute demand in law enforcement, cyber-security and national security.

Learning Outcomes

By the end of the module the student should be able to:

  • Understand various modalities of device fingerprints and ways for extracting and enhancing device fingerprints from digital content
  • Understand forensic applications of device fingerprints in source device identification, content/device linking, source-oriented image clustering and content integrity verification
  • Understand data hiding techniques and their applications in copyright protection and content authentication
  • Understand data hiding techniques and their applications in steganography and steganalysis
  • Understand theoretical and practical challenges, including counter-forensics and counter-counter-forensics


  • • Various modalities of device fingerprints
    • Extraction and representation of device fingerprints
    • Enhancement of device fingerprints
    • Source device identification based on device fingerprints
    • Content/device linking based on device fingerprints
    • Content integrity verification based on device fingerprints
    • Source-oriented image/video clustering based on device fingerprints
    • Digital content hashing
    • Data hiding
    • Digital watermarking for copyright protection
    • Digital watermarking for content authentication
    • Steganography
    • Steganalysis
    • Counter-forensics and counter-counter-forensics


  • J. Lukas, J. Fridrich, and M. Goljan, “Digital camera identification from sensor pattern noise,” IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, pp. 205–214, 2006.
  • M. Chen, J. Fridrich, M. Goljan, and J. Luk´as, “Determining image origin and integrity using sensor noise,” IEEE Transactions on Information Forensics and Security, vol. 3, no. 1, pp. 74–90, 2008.
  • C.-T Li, "Source Camera Identification Using Enhanced Sensor Pattern Noise," IEEE Transactions on Information Forensics and Security, vol. 5, no. 2, pp. 280 – 287, June 2010
  • C.-T. Li, "Source Imaging Device Linking Using Filtered Sensor Pattern Noise," in Proc. 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), London, UK, 3 December, 2009
  • X. Lin and C.-T. Li, "Preprocessing Reference Sensor Pattern Noise via Spectrum Equalization," IEEE Transactions on Information Forensics and Security, vol. 11, no. 1, pp. 126-140, Jan 2016
  • X. Lin and C.-T. Li, "Enhancing Sensor Pattern Noise via Filtering Distortion Removal,” IEEE Signal Processing Letter, vol. 23, no. 3, pp. 381 - 385, March 2016
  • C.-T. Li (Ed), "Multimedia Forensics and Security", IGI Global, Hershey, PA. USA, April 2008. (ISBN: 978-1-59904-869-7)
  • I. J. Cox, M. L. Miller, and J. A. Bloom, “Digital Watermarking and Steganography,” Morgan Kaufmann, 2007
  • C.-T. Li and Y. Yuan, "Digital Watermarking Scheme Exploiting Non-Deterministic Dependence for Image Authentication," Optical Engineering, vol. 45, no. 12, pp. 127001-1 ~ 127001-6, Dec. 2006
  • C.-T. Li, "Digital Fragile Watermarking Scheme for Authentication of JPEG Images," IEE Proceedings - Vision, Image, and Signal Processing, vol. 151, no. 6, pp. 460 - 466, 2004


Two-hour examination (70%), coursework (30%)


20 one-hour lectures plus 10 one-hour seminars plus 10 one-hour workshops

Jalote P, Fault Tolerance in Distributed Systems, Prentice Hall, 1994.
Lynch N, Distributed Algorithms, Morgan Kauffman, 1996.
Gouda M, Elements of Network Protocol Design, John Wiley, 1998.
  • Background: development and scope of social informatics; practical goals.
  • Understanding individual behaviour: perception, memory and action.
  • Modelling human interaction with digital systems.
  • Design methodologies and notations.
  • Techniques and technologies: dialogue styles, information visualisation.
  • Designer-user relations: iteration, prototyping.
  • Evaluation: formative and summative; performance and learnability.
  • Mobile computing and devices: novel interfaces; ubiquitous computing.
  • Organisational factors: understanding the workplace; resistance; dependability.
Innovation processes at scale: social shaping of IT, actor-network theory, co-production.