1. Multimedia Data Hiding: What, Why, and How?
Kaushal Solanki
Vision Research Laboratory
University of California, Santa Barbara
Contact info:

2. Outline Preview Introduction: What is Data Hiding?
Applications: Why Data Hiding?
Techniques: How to Hide Data?
Principles
State of the art
Challenges
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3. The “digital” age has come…
For a moment, sit back, and think how much our lives have changed in the past decade itself.
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4. Telecommunication: “Then”
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5. Digital age telecommunication
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6. Information access: “Then”
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7. Digital age Information access
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8. We are migrating from analog to digital…
Digital cameras replacing their analog counterpart.
Audio CDs replacing cassettes.
DVDs replacing video cassettes.
Digital video cameras replacing analog ones.
Digital high definition TVs have a very good future.
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9. The endangered species…
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10. The future…
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11. The future…
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12. The digital representation
Many advantages
Lossless and convenient to store.
Lossless and convenient transmission.
Lossless and easy to make a copy.
Extremely easy to share across many users.
Easy to edit.
Durable, inexpensive and easily searchable retrieval.
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13. But … Some advantages have a dark side : Piracy is very easy.
Sharing and peer to peer transfer of multimedia and software.
Kazaa, Limewire, Morpheus, Napster, etc.
Unreliable
Cannot be used as evidence in court.
Can be misused to create false impressions.
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14. Outline Preview Introduction: What is Data Hiding?
Applications: Why Data Hiding?
Techniques: How to Hide Data?
Principles
State of the art
Challenges
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15. A potential Solution Multimedia Data Hiding
Idea: Imperceptibly embed copyright or other information into the multimedia data.
Challenge: Maintain imperceptibility while embedding sufficient information and be robust against manipulations.
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16. Multimedia Data Hiding: An Emerging Field
Very young and growing field.
Well over 90% publications in past 7 years.
Why so?
Digital pictures, video, and sound tracks gaining popularity.
Widespread illegal copying and piracy.
Emergence of peer-to-peer file sharing networks.
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17. Multimedia Data Hiding: A multidisciplinary field
Image and signal processing
Cryptography
Communication theory
Error correction coding
Signal compression
Visual perception theory
Information theory
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18. Ever heard of… Steganography Watermarking Fingerprinting
Authentication
Information hiding, or information embedding
Data embedding
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19. What is Data Hiding?
Data hiding is the process by which a message signal, or signature is imperceptibly embedded within a host data set to form a composite signal.
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20. A data hiding system Multimedia “Host” Embedding Algorithm Decoding
Secret
message
Attack Channel
Secret
message
Composite data
Secret “key”
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21. Design Issues Transparency: No perceptual degradation
Robustness: Survive benign or malicious attacks
Volume: Embed as much information as possible
Blind/Non-Blind
Performance: The encoding/decoding complexity
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22. Other Design Issues Undetectability:
Significant for steganographic applications.
How much information can I transmit without it being detected?
Graceful degradation:
Less severe attack => More information recovered
Not considered widely in the literature!
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23. Tradeoffs Robustness Undetectability Capacity Robustness Performance
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24. Outline Preview Introduction: What is Data Hiding?
Applications: Why Data Hiding?
Techniques: How to Hide Data?
Principles
State of the art
Challenges
20/11/2004

25. Applications: Why Data Hiding?
Steganography
Copyright protection
Document authentication
Annotation of medical images
Tamper detection and localization
Seamless upgrade of multimedia
Secure communication in mobile environments
Embedding meta data into multimedia
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26. Applications(I): Steganography
“Steganography is the art and science of communicating in a way that hides the existence of the communication itself”
Prisoners’ problem, Simmons[1983]
Alice and Bob are in jail and wish to hatch an escape plan. All their communication pass through warden Willie, who would frustrate their plan by throwing them into secret confinement if he detects any communication between them.
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27. Applications(I): Steganography
Robustness
Undetectability
Capacity
Secure Steganography
Naïve Steganography
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28. Applications(II): Digital Watermarking
Copyright protection: Owner identification and proof of ownership.
Digital fingerprinting: Hide unique signatures into different copies of the same content before distribution.
Design issues:
Robustness to a variety of malicious attacks.
Transparency: preserve the ‘value’ of the content.
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29. Applications(II): Digital Watermarking
Robustness
Undetectability
Capacity
Digital Watermarking
Secure Steganography
Naïve Steganography
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30. Applications (III): Document Authentication
Embedding information robustly in documents to authenticate them.
Can be used for authenticating passport, identification cards, etc.
Design issues:
Robustness to print-scan, with control on printing.
Fast decoding.
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31. Applications (IV) : Tamper Detection and Localization
Need protection against tampering of “evidence” within a forensic image.
Appropriately designed hiding system can decode the hidden information robustly and localize the tampered area.
Design issues:
Robustness against tampering (of course!).
Transparency.
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32. Tamper Localization Example
6,301 bits hidden against image tampering
After decoding is performed, the tampered region can be automatically localized
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33. Applications (V): Other applications
Seamless upgrade of multimedia.
Secure communication in mobile environments.
Embedding meta data in multimedia.
Broadcast monitoring.
We believe that many other applications will come up driven by the availability of the technology
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34. Outline Preview Introduction: What is Data Hiding?
Applications: Why Data Hiding?
Techniques: How to Hide Data?
Principles
State of the art
Challenges
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35. Broad Classification Least Significant Bit (LSB) modulation
Modify the LSB with the data to be embedded.
Spread Spectrum (SS) modulation
Involves adding a spread version of the data.
Quantization Index Modulation (QIM)
Hide data in the choice of vector quantizer
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36. LSB Hiding Modify the least significant bit of the image.
Can be either in spatial or transform domain.
Advantages:
Very simple method – low complexity.
Virtually no perceptual distortion (spatial LSB hiding).
Disadvantages:
Not robust against attacks.
Can be easily “detected”.
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37. Spread Spectrum Hiding
Probably most widely used watermarking method.
Add a spread version of the watermark to the host image, either in spatial or transform domain.
Advantages:
Robust against various attacks.
Allows perceptual shaping.
Disadvantages:
Blind detection possible, but difficult.
Host signal interference for blind detection.
Low capacity.
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38. QIM Hiding Hide data into choice of quantizer.
Provably “good”, under certain assumptions [Chen & Wornell 2001].
Advantages:
Blind decoding comes “naturally” (no host signal interference).
Robust against various attacks.
High capacity constructions possible.
Disadvantages:
Perceptual adaptation is not straightforward.
Steganalysis is shown to be easier than SS.
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39. Embed data in choice of vector quantizers
Red Quantizer: Embed 0
Blue Quantizer: Embed 1
Embed data in choice of vector quantizers
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40. Scalar QIM
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41. Scalar QIM Example
Consider the case of odd-even quantizers. Quantization to odd value is a ‘1’, and to even values is a ‘0’.
Let us assume that the host coefficient value is
To embed ‘1’, we send the nearest odd number, i.e., 7.0
Likewise, to embed ‘0’, we send 6.0
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42. Outline Preview Introduction: What is Data Hiding?
Applications: Why Data Hiding?
Techniques: How to Hide Data?
Principles
State of the art
Challenges
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43. “Well known” Principles
Embed in transform domain
DCT, Wavelet, DFT, etc.
Key dependent transformaion
Embed in high-variance bands [Moulin & Mihcak]
Embed in low and mid frequency bands.
Use error correction codes for achieving robustness.
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44. Information-theoretic results
Data hiding can be considered as communication with side information at the encoder.
Message = data to be transmitted.
Channel = “attack”.
Side information = host.
Writing on dirty paper [Costa `82].
Showed that capacity is same for the side information case as one without side information.
That is ‘no host interference’ is possible.
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45. Summary of Info. theoretic results
Formulation
D1 denotes mean squared embedding induced distortion.
D2 denotes mean Squared attack distortion.
Data Hiding Capacity with AWGN attack, termed Vector Capacity is (for small D1 and D2 regime): [Costa]
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46. Outline Preview Introduction: What is Data Hiding?
Applications: Why Data Hiding?
Techniques: How to Hide Data?
Principles
State of the art
Challenges
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47. State of the art Steganography and Steganalysis Image Data Hiding
Techniques that are undetectable using histograms have been developed.
Stochastic QIM [Moulin et al `04].
Steganalysis of block-DCT hiding [Fridrich et al]
Image Data Hiding
Robust image-adaptive hiding: [Solanki et al `04].
Reversible data hiding
Image-in-image hiding [Solanki et al `03]
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48. State of the art (II) Surviving Geometric Attacks
Rotation, Scale, and Translation (RST) invariant spread spectrum data hiding [Ruanaidh and Pun `98].
Use tessellation points in images [Bas et al `02].
Print-and-scan resilient data hiding
Modeling spatial and geometric distortions separately [Lin et al `99].
SELF: Selective embedding in low frequencies scheme [Solanki et al `04].
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49. Outline Preview Introduction: What is Data Hiding?
Applications: Why Data Hiding?
Techniques: How to Hide Data?
Principles
State of the art
Challenges
20/11/2004

50. Challenges Ahead Steganography Steganalysis Digital watermarking
Creating methods that use “memory”.
Steganalysis
Use continuity in images to detect low rate hiding.
Digital watermarking
Consistently surviving geometric attacks, such as Stirmark random bending.
Survive print-scan reliably.
Tuning data hiding schemes for specific applications, such as tamper detection.
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51. Thank You