Data Throughput and Data Throughput and Robustness Studies in a Robustness Studies in a

New Digital Image New Digital Image WWatermarking Technique atermarking Technique

Based upon Based upon AAdaptive daptive SSegmentation & egmentation &

SSpace-pace-FFrequency requency RRepresentation (epresentation (WASSFRWASSFR))

byNeelu Sinha

Fairleigh Dickinson University

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ContentsContents• Introduction & BackgroundIntroduction & Background

– Requirements & Algorithmic Design Requirements & Algorithmic Design Issues Issues

– WASSFRWASSFRAdaptive SegmentationAdaptive SegmentationAdaptive Space-Frequency Adaptive Space-Frequency

RepresentationRepresentationPsycho-visual considerationsPsycho-visual considerations

• Description of Digital Rights Description of Digital Rights Management SystemManagement System

• Robustness StudiesRobustness Studies

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IntroductionIntroduction

• Extensive use and distribution of Extensive use and distribution of digitized media in the Internet agedigitized media in the Internet age

• Need for robust watermarking Need for robust watermarking schemes to protect, detect and schemes to protect, detect and verify ownership of dataverify ownership of data

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IntroductionIntroduction

Digital Watermark

Watermark (W)

Original Data (I)

Key (k)

Watermarked data (I’)

Watermark Insertion

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IntroductionIntroduction

WatermarkDetection

Watermark (W) or Original data (I)

Watermarked data (I’)

Key (k)

Confidence Measure or Watermark (W)

Watermark Detection

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BACKGROUNDBACKGROUND

• ImperceptibilityImperceptibility

• Robustness/RedundancyRobustness/Redundancy– must be robust to signal processing must be robust to signal processing

distortions & attacksdistortions & attacks

Principles in the Design of a Watermarking Algorithm

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BACKGROUNDBACKGROUND

• Choice of a workspace (e.g., signal domain)Choice of a workspace (e.g., signal domain)– Space, frequency (DFT, DCT), Mellin-Fourier Space, frequency (DFT, DCT), Mellin-Fourier

transform, wavelet transform, etc.transform, wavelet transform, etc.

• Choice of host location / watermark bit Choice of host location / watermark bit formatterformatter– fixed, random, signal-adaptive, spread-spectrum, fixed, random, signal-adaptive, spread-spectrum,

etc.etc.

• Merging of a watermark and the coverMerging of a watermark and the cover– amplitude and/or phase modulation, coefficient pair amplitude and/or phase modulation, coefficient pair

etc.etc.

Structure of a Practical Watermarking Algorithm

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BACKGROUNDBACKGROUND

• Data RateData Rate

• Signal DistortionSignal Distortion

• Robustness v/s Data ThroughputRobustness v/s Data Throughput– bit error rate v/s information rate for a bit error rate v/s information rate for a

given signal qualitygiven signal quality

Performance Metrics for a watermarking algorithm

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BACKGROUND: BACKGROUND: Cox’s Cox’s 1997 Proposal1997 Proposal

• [Cox et al. 1997] incorporation of a [Cox et al. 1997] incorporation of a watermark into the frequency transform watermark into the frequency transform of the image.of the image.

• Watermark is incorporated into Watermark is incorporated into perceptually most significant componentsperceptually most significant components

• Psycho-visual models for multi-Psycho-visual models for multi-component masking are usedcomponent masking are used

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Watermarking technique Watermarking technique based upon Adaptive based upon Adaptive

Segmentation and Space-Segmentation and Space-Frequency representation Frequency representation

(WASSFR)(WASSFR)Based on following principlesBased on following principles

• Signal adaptive criterion allows for Signal adaptive criterion allows for robust watermark and higher bit ratesrobust watermark and higher bit rates

• Psycho-visual principles must be used Psycho-visual principles must be used for imperceptibility as well as for imperceptibility as well as robustnessrobustness

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WASSFRWASSFR

• Adaptive segmentation of the image Adaptive segmentation of the image based on a based on a novel “entropy” criterionnovel “entropy” criterion..

• Selection of a suitable space-frequency Selection of a suitable space-frequency representation for each segmentrepresentation for each segment– to allow for highest watermark bit rateto allow for highest watermark bit rate

• Identification of perceptually most Identification of perceptually most significant component in the significant component in the transformed image.transformed image.

• Insertion of the watermarkInsertion of the watermark

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Adaptive segmentationAdaptive segmentation

• Separation of an image into regions with Separation of an image into regions with similar attribute: similar attribute: in terms of susceptibility to in terms of susceptibility to distortions in space and frequency domaindistortions in space and frequency domain

– uniform intensity or textured regions less uniform intensity or textured regions less affected by controlled noise injection in affected by controlled noise injection in frequency domainfrequency domain

– edges less affected if noise profile is controllable edges less affected if noise profile is controllable in space domainin space domain

• Perceptually significant components are Perceptually significant components are easier to identify for a suitably segmented easier to identify for a suitably segmented imageimage

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Adaptive segmentationAdaptive segmentationNovel “Entropy” CriteriaNovel “Entropy” Criteria

• Separation of an image into regions Separation of an image into regions with similar attributes: a new with similar attributes: a new ““entropy“ criterion ientropy“ criterion is useds usedF(x,y) = |(I(x,y)| / R1 |I(x,y)|

Entropy: R1 = R1 F(x,y) log2 [1/F(x,y)]

• Signals presence of region Signals presence of region boundaries and/or discontinuitiesboundaries and/or discontinuities

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Space-Frequency Space-Frequency RepresentationRepresentation

• Instead of using a pure frequency Instead of using a pure frequency domain approach (as used by Cox et domain approach (as used by Cox et al.) use a set of space-frequency al.) use a set of space-frequency representationsrepresentations– Space representationSpace representation

* If entropy <= T1If entropy <= T1

– 2-D Frequency representation (DCT)2-D Frequency representation (DCT)* If T2 < entropyIf T2 < entropy

– 2-D Wavelet representation2-D Wavelet representation* If T1 < entropy <= T2If T1 < entropy <= T2

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Psycho-visual Considerations Psycho-visual Considerations

• identify the perceptually significant identify the perceptually significant components (potential carriers) in a components (potential carriers) in a (transformed) segment(transformed) segment

• compute the relative strength of the compute the relative strength of the watermarkwatermark

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Preliminary ResultsPreliminary Results

Original Lena image (left) and corresponding watermarked Original Lena image (left) and corresponding watermarked image (right) with 16 kbits of embedded dataimage (right) with 16 kbits of embedded data

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Preliminary ResultsPreliminary Results• In many typical 512x512 images 10-In many typical 512x512 images 10-

16 kbits of data (about 0.1 16 kbits of data (about 0.1 bits/pixel) may be embedded with bits/pixel) may be embedded with little or no loss in perceived quality.little or no loss in perceived quality.

• Of course, a lot of these bits will be Of course, a lot of these bits will be used in making the watermark used in making the watermark robust for real-world applicationsrobust for real-world applications– Encryption, Synchronization, Encryption, Synchronization,

Repetition/channel coding, etc.Repetition/channel coding, etc.

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Digital Rights Digital Rights Management SystemManagement System

• Watermark/Information Embedding Watermark/Information Embedding TechniqueTechnique

• Encryption Encryption

• Synchronization MechanismSynchronization Mechanism

• Channel CodingChannel Coding

• Selection of information pattern and Selection of information pattern and embedding locationembedding location

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Digital Rights Digital Rights Management SystemManagement System

• Encryption Encryption – Design considerations of the digital rights Design considerations of the digital rights

management scheme is based on its management scheme is based on its applications!applications!

– For copyright protection applications, the For copyright protection applications, the secrecy of the embedded information secrecy of the embedded information needs to be maintained, thus requiring a needs to be maintained, thus requiring a secret key for the embedding/extraction secret key for the embedding/extraction process.process.

– On the other hand, for image database On the other hand, for image database indexing application, security is not an indexing application, security is not an issue.issue.

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Digital Rights Digital Rights Management SystemManagement System

• Synchronization Mechanism Synchronization Mechanism – A fixed bit pattern is added to the A fixed bit pattern is added to the

information sequenceinformation sequence

– This pattern can also function as a key This pattern can also function as a key during the detection stage.during the detection stage.

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Digital Rights Digital Rights Management SystemManagement System

• Channel CodingChannel Coding– Direct link between digital watermarking Direct link between digital watermarking

and communications theoryand communications theory

– Channel codes (e.g., redundancy) Channel codes (e.g., redundancy) increase the robustness of information increase the robustness of information transmissiontransmission

– A channel coding technique that performs A channel coding technique that performs well under erasures/false detection was well under erasures/false detection was developed for this applicationdeveloped for this application

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Robustness StudiesRobustness Studies

• Robustness/Bit error rate measurementRobustness/Bit error rate measurement– Using inspiration from Communications Using inspiration from Communications

Theory, robustness measured in terms of Theory, robustness measured in terms of bit error rate, which is the number of bit error rate, which is the number of information bits which may be received information bits which may be received corrupt for a single information bit.corrupt for a single information bit.

– Studied as a function of data throughput Studied as a function of data throughput (bitrate in bits/pixel)(bitrate in bits/pixel)

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Robustness StudiesRobustness Studies

• Selection of an Image DatabaseSelection of an Image Database– Size of data and nature of data both Size of data and nature of data both

have an impact on the robustnesshave an impact on the robustness

– Various classes of images usedVarious classes of images used

• AttacksAttacks– Geometric and removal attacksGeometric and removal attacks

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Images: ClassicsImages: Classics

© Signal and Image Processing Institute, © Signal and Image Processing Institute, University of Southern CaliforniaUniversity of Southern California

Lena Peppers

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Images: Images: Lines & EdgesLines & Edges

Skyline Arch © © Robert E. BarberRobert E. BarberNature PhotographyNature Photography

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Images: Images: Textures & Fine Textures & Fine DetailsDetails

Kid Kid © Karel de © Karel de

GendreGendre

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Images: Reduced Color Set Images: Reduced Color Set & Dark Colors & Dark Colors

Brandy Rose © Toni © Toni LankerdLankerd

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Images: Computer Images: Computer GeneratedGenerated

Waterfall © Sascha Ledinsky© Sascha Ledinsky

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AttacksAttacks

We considered:We considered:

• JPEG compressionJPEG compression

• Geometric TransformationsGeometric Transformations– Horizontal Flip, Rotation, CroppingHorizontal Flip, Rotation, Cropping

• Noise additionNoise addition

Geometric attacksGeometric attacks Removal attacksRemoval attacks

Cryptographic attacksCryptographic attacks Protocol attacksProtocol attacks

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Experimental ResultsExperimental Results

Attack Attack TypeType

Attack Attack StrengthStrength

InformatioInformation Raten Rate

Bit Bit ErrorError

JPEG CompJPEG Comp 6:16:1 0.001bpp0.001bpp 1.e-41.e-4

JPEG CompJPEG Comp 6:16:1 0.010.01 1.e-21.e-2

Geom. TranGeom. Tran Hor. FlipHor. Flip 0.010.01 1.e-51.e-5

Geom. TranGeom. Tran RotationRotation 0.010.01 1.e-51.e-5

Geom. TranGeom. Tran CroppingCropping 0.0010.001 1.e-51.e-5

Geom. TranGeom. Tran CroppingCropping 0.010.01 1.e-31.e-3

Geom. TranGeom. Tran ScalingScaling 0.010.01 1.e-41.e-4

NoiseNoise SNR=55dBSNR=55dB 0.010.01 1.e-31.e-3

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ConclusionsConclusions

• A New Digital Rights Management A New Digital Rights Management System based on WASSFR was System based on WASSFR was describeddescribed

• Experimental results indicate Experimental results indicate robustness of the scheme to image robustness of the scheme to image processing distortions and attacksprocessing distortions and attacks

• Results quantify trade-offs between Results quantify trade-offs between information throughput and information throughput and robustness.robustness.