This document provides an overview of fundamentals of watermarking and data hiding. It discusses basic techniques, performance analysis in terms of error probabilities and capacity, and applications to images and advanced topics. The document outlines the tutorial, which covers data hiding, watermarking and steganography applications, embedding data in images, basic properties like fidelity and robustness, attack models, and theoretical concepts from information theory, game theory, coding theory and cryptography that are relevant to understanding fundamental limits.

1. Fundamentals of Watermarking and Data Hiding
Pierre Moulin
University of Illinois at Urbana–Champaign
Dept of Electrical and Computer Engineering
moulin@ifp.uiuc.edu
July 9, 2006 ISIT Tutorial, Seattle
c 2006 by Pierre Moulin. All rights reserved.
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2. Outline
1. Overview
2. Basic Techniques
3. Binning Schemes and QIM Codes
4. Performance Analysis: Error Probabilities
5. Performance Analysis: Capacity
6. Applications to Images & Advanced Topics
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3. SESSION 1: OVERVIEW
• Data hiding, watermarking, steganography
• Basic properties: fidelity, payload, robustness, security
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4. Some Reading
• Books:
Digital Watermarking, by I. Cox, M. Miller, J. Bloom,
Morgan-Kaufmann, 2002
Information Hiding Techniques for Steganography and Digital
Watermarking, by S. Katzenbeisser and F. Petitcolas, Eds.,
Artech House, 2000
Information Hiding: Steganography and Watermarking,
by N. Johnson, Z. Duric and S. Jajodia, Kluwer, 2000
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5. • New IEEE Transactions on Information Forensics and Security
(quarterly, inaugural issue in March 2006)
• Special issues of various IEEE journals, 1999 – 2005
• Annual Information Hiding Workshops
• Watermarking newsletter: www.watermarkingworld.org
• www.ifp.uiuc.edu/˜moulin
• Tutorial paper “Data Hiding Codes” by P. Moulin and
R. Koetter, Proceedings IEEE, December 2005.
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6. Multimedia Security
• Dissemination of digital documents
• Owner identification
• Forgery detection
• Identification of illegal copies
• Intellectual protection
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7. Authentication
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8. 8

9. Media Elements
• Audio
• Images
• Video
• Graphics
• Documents
• Computer programs
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10. Nonadversarial Applications
• Database annotation
• Information embedding, e.g., audio in images, text in host
signals (movie subtitles, financial data, synchronization signals)
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11. Data Hiding
• Embed data in covertext (high payload)
• Perceptual similarity requirement
• Multimedia database management
• Covert communications (military, spies, etc.)
• Steganography (στ γανω γραφω, covert writing):
conceal existence of hidden message
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12. Watermarking
• Hide a few bits of information
• Original and modified signals should be perceptually similar
• Application to digital cameras, TV, DVD video, audio
• Authentication
• Transaction tracking
• Broadcast monitoring
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13. Fingerprinting
• Fingerprinter marks several copies of original and distributes
copies to users 1, 2, · · · , L
• Each mark is different
• Users may collude to “remove” watermarks
• Applications: copy control, traitor tracing
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14. Summary of Applications
Applications
Watermarking authentication, copyright protection
Data hiding covert communications, database annotation,
information embedding
Steganography covert communications
Fingerprinting copy control, traitor tracing
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15. A Brief History
• Tattoo hidden message on head of slave (ancient Greeks)
• Invisible ink
• Secret point patterns
• Watermarks in paper (Italy, 13th century)
• Digital watermarking: early 1990’s
• Standardization attempts:
SDMI (music), ISO (MPEG video)
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16. Hiding Data in Images
secret
key k
Encoder
original image S watermarked image X
Picture taken by Alice on
January 1, 2000. This message
is going to be embedded forever
in this picture. I challenge you
to remove the message without
substantially altering the picture.
1001001101001110100...............101
binary representation
Decoder
Picture taken by Alice on
January 1, 2000. This message
is going to be embedded forever
in this picture. I challenge you
to remove the message without
substantially altering the picture.
Decoded message
1001001101001110100...............101
Decoded binary
message
secret key k
Attack
Pirate
11011000...01
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17. Decoder’s Task
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18. Attacks on Images
Original JPEG, QF=10 4 × 4 median filtering
Gaussian filter (σ = 3) Rotated by 10 degrees Random bend
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19. Basic Properties
• Fidelity (in terms of signal distortion metric)
• Payload (number of transmitted bits)
• Robustness (against adversary)
• Security (cryptanalysis of randomized code)
• Detectability (by steganalyzers/eavesdroppers)
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20. System Issues
• System complexity
• Does decoder know host signal?
(public vs private watermarking)
• Security level?
• Reliance on private or public cryptographic system?
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21. Attack Models
• No attack
• Deterministic attacks (reversible & irreversible)
• Stochastic attacks (memoryless & stationary)
• Code breaking
• System attacks (e.g., ambiguity, sensitivity & scrambling)
• Benchmarking (e.g., Stirmark)
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22. Attacks
Attack Type Examples
Memoryless independent noise,
random pixel replacement
Blockwise memoryless JPEG compression
Attacks with stationary noise,
”statistical regularity” spatially invariant filtering,
some estimation attacks
Deterministic compression, format changes
Arbitrary attacks cropping, permutations,
desynchronization,
nonstationary noise
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23. Basic Theoretical Concepts
• Information theory
• Game theory
• Detection and estimation theory
• Coding theory
• Cryptography
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24. Purposes of an Information-Theoretic Approach
• make appropriate simplifying assumptions to understand
fundamental limits of IH and optimally design algorithms
• provide new insights into IH
• provide a precise framework for evaluating any IH algorithm
• develop approach that generalizes easily to related problems
Caution: cost of mismodeling may be severe in game with
opponent!
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