際際滷

際際滷Share a Scribd company logo
Fundamentals of Watermarking and Data Hiding
Pierre Moulin
University of Illinois at UrbanaChampaign
Dept of Electrical and Computer Engineering
moulin@ifp.uiuc.edu
July 9, 2006 ISIT Tutorial, Seattle
c 2006 by Pierre Moulin. All rights reserved.
1
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
2
SESSION 1: OVERVIEW
 Data hiding, watermarking, steganography
 Basic properties: 鍖delity, payload, robustness, security
3
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
4
 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.
5
Multimedia Security
 Dissemination of digital documents
 Owner identi鍖cation
 Forgery detection
 Identi鍖cation of illegal copies
 Intellectual protection
6
Authentication
7
8
Media Elements
 Audio
 Images
 Video
 Graphics
 Documents
 Computer programs
9
Nonadversarial Applications
 Database annotation
 Information embedding, e.g., audio in images, text in host
signals (movie subtitles, 鍖nancial data, synchronization signals)
10
Data Hiding
 Embed data in covertext (high payload)
 Perceptual similarity requirement
 Multimedia database management
 Covert communications (military, spies, etc.)
 Steganography ( 粒留僚 粒留, covert writing):
conceal existence of hidden message
11
Watermarking
 Hide a few bits of information
 Original and modi鍖ed signals should be perceptually similar
 Application to digital cameras, TV, DVD video, audio
 Authentication
 Transaction tracking
 Broadcast monitoring
12
Fingerprinting
 Fingerprinter marks several copies of original and distributes
copies to users 1, 2, 揃 揃 揃 , L
 Each mark is di鍖erent
 Users may collude to remove watermarks
 Applications: copy control, traitor tracing
13
Summary of Applications
Applications
Watermarking authentication, copyright protection
Data hiding covert communications, database annotation,
information embedding
Steganography covert communications
Fingerprinting copy control, traitor tracing
14
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 1990s
 Standardization attempts:
SDMI (music), ISO (MPEG video)
15
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
16
Decoders Task
17
Attacks on Images
Original JPEG, QF=10 4  4 median 鍖ltering
Gaussian 鍖lter ( = 3) Rotated by 10 degrees Random bend
18
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)
19
System Issues
 System complexity
 Does decoder know host signal?
(public vs private watermarking)
 Security level?
 Reliance on private or public cryptographic system?
20
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)
21
Attacks
Attack Type Examples
Memoryless independent noise,
random pixel replacement
Blockwise memoryless JPEG compression
Attacks with stationary noise,
statistical regularity spatially invariant 鍖ltering,
some estimation attacks
Deterministic compression, format changes
Arbitrary attacks cropping, permutations,
desynchronization,
nonstationary noise
22
Basic Theoretical Concepts
 Information theory
 Game theory
 Detection and estimation theory
 Coding theory
 Cryptography
23
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!
24

More Related Content

shilpa

  • 1. Fundamentals of Watermarking and Data Hiding Pierre Moulin University of Illinois at UrbanaChampaign Dept of Electrical and Computer Engineering moulin@ifp.uiuc.edu July 9, 2006 ISIT Tutorial, Seattle c 2006 by Pierre Moulin. All rights reserved. 1
  • 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 2
  • 3. SESSION 1: OVERVIEW Data hiding, watermarking, steganography Basic properties: 鍖delity, payload, robustness, security 3
  • 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 4
  • 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. 5
  • 6. Multimedia Security Dissemination of digital documents Owner identi鍖cation Forgery detection Identi鍖cation of illegal copies Intellectual protection 6
  • 8. 8
  • 9. Media Elements Audio Images Video Graphics Documents Computer programs 9
  • 10. Nonadversarial Applications Database annotation Information embedding, e.g., audio in images, text in host signals (movie subtitles, 鍖nancial data, synchronization signals) 10
  • 11. Data Hiding Embed data in covertext (high payload) Perceptual similarity requirement Multimedia database management Covert communications (military, spies, etc.) Steganography ( 粒留僚 粒留, covert writing): conceal existence of hidden message 11
  • 12. Watermarking Hide a few bits of information Original and modi鍖ed signals should be perceptually similar Application to digital cameras, TV, DVD video, audio Authentication Transaction tracking Broadcast monitoring 12
  • 13. Fingerprinting Fingerprinter marks several copies of original and distributes copies to users 1, 2, 揃 揃 揃 , L Each mark is di鍖erent Users may collude to remove watermarks Applications: copy control, traitor tracing 13
  • 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 14
  • 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 1990s Standardization attempts: SDMI (music), ISO (MPEG video) 15
  • 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 16
  • 18. Attacks on Images Original JPEG, QF=10 4 4 median 鍖ltering Gaussian 鍖lter ( = 3) Rotated by 10 degrees Random bend 18
  • 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) 19
  • 20. System Issues System complexity Does decoder know host signal? (public vs private watermarking) Security level? Reliance on private or public cryptographic system? 20
  • 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) 21
  • 22. Attacks Attack Type Examples Memoryless independent noise, random pixel replacement Blockwise memoryless JPEG compression Attacks with stationary noise, statistical regularity spatially invariant 鍖ltering, some estimation attacks Deterministic compression, format changes Arbitrary attacks cropping, permutations, desynchronization, nonstationary noise 22
  • 23. Basic Theoretical Concepts Information theory Game theory Detection and estimation theory Coding theory Cryptography 23
  • 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! 24