Digital Watermarking With Digital Watermarking With Phase Dispersion AlgorithmPhase Dispersion Algorithm

Team 1 Final PresentationTeam 1 Final Presentation

SIMG 786 SIMG 786

Advanced Digital Image ProcessingAdvanced Digital Image Processing

Mahdi Nezamabadi, Mahdi Nezamabadi,

Chengmeng Liu, Chengmeng Liu,

Michael SuMichael Su

Motivating ScenarioMotivating ScenarioMotivating ScenarioMotivating Scenario

• Alice creates a 3D shape,and publishes it on the web.

• Bob sells it as his own.Bob sells it as his own.

• How can Alice prove ownership?How can Alice prove ownership?(and make Bob pay her a lot of (and make Bob pay her a lot of money)money)

• Alice creates a 3D shape,and publishes it on the web.

• Bob sells it as his own.Bob sells it as his own.

• How can Alice prove ownership?How can Alice prove ownership?(and make Bob pay her a lot of (and make Bob pay her a lot of money)money)

The solution is…The solution is…

• An invisible, robust digital watermark and put it on the image which can be used for proving the ownership.

• It has been applied in copyright marking business.

• It can be also applied for digital multimedia

Digital Watermarking With Phase Digital Watermarking With Phase Dispersion AlgorithmDispersion Algorithm

• An algorithm for robust, invisible watermarking.• Use the spread-spectrum technique which was

first in communications for hiding the information.

• Uses this characteristics to hide and extract information.

• It can embed both iconic images and binary strings in an image.

• It can handle various types of attacks.

Malicious AttacksMalicious AttacksMalicious AttacksMalicious Attacks

Adding noise

Adding another watermark

Rescale

Lossy compression

Geometric distortion

Cropping

Print and scan

Adding noise

Adding another watermark

Rescale

Lossy compression

Geometric distortion

Cropping

Print and scan

Embedding process illustrationEmbedding process illustration

Watermark extraction processWatermark extraction process

Indices for image differenceIndices for image difference

• MSE (Mean square error)

2

1 1

1

N

i

M

j

i,jIi,jI'NM

I,I'MSE

• Correlation factor

),(),(

),(),('

yxMyxM

yxMyxMCF

Similarity vs. α

• Similarity is measured by cross correlation between original and extracted log

• 64 tiles were used in embedding

• The α controls the visibility of the watermark logo in the watermarked image

• The α also depends on the number of tiles

Implementation of Binary Message Implementation of Binary Message template function 1template function 1

• embedding binary information consists of representing the one and zero bits by positive delta function and black that are placed in predefined and unique locations within the message image.

• It consisted of concentric circles with equal increments in radius and random angular displacement.

• A 64 bits template is shown on left

• The error rate is 0 for this 64 bits template

Implementation of Binary Message Implementation of Binary Message template function 2template function 2

• 650 bits template function is shown on the left

• 650 bits can embed 32 characters by repeating them 5 times with no compression

• The error rate is 0.46% for this 650 bits template, that means the probability for get a wrong bit is 9.7e-8

Rotation/Scale DetectionThresholding

Rotation/Scale DetectionImage rotation

Recovering image from Recovering image from distorted imagedistorted image

How it works?How it works?

x f1(x,y)

y f2(x, y)For example:

x a11x a12y a13

y a21x a22y a23

In matrix form it will be:

x

y

a11 a12 a13

a21 a22 a23

x

y

1

Matirx formMatirx form

A a11 a12 a13

a21 a22 a23

XYo x

y

XYi x

y

1

XYo AXYi

There is a standard method to solve above equation for matrix A. For above example we need at least six points (six equations) to solve for six unknown coefficients.

Matrix form continued…Matrix form continued…

XYo AXYi

XYo (XYi ) AXYi (XYi )

XYo (XYi ) Inverse(XYi (XYi ) ) AXYi (XYi ) Inverse(XYi (XYi ) )

XYi (XYi ) Inverse(XYi (XYi ) ) 1 1 1

1 1 1

1 1 1

A XYo (XYi ) Inverse(XYi (XYi ) )

Apply matrix A and some extra Apply matrix A and some extra simple image processingsimple image processing

• Apply matrix A to whole image and calculate new coordinates of reconstructed image.

• Interpolate for in-between points.• If necessary zero-padding should be

applied.• Trim the image to have integer number of

tiles in each direction.

Rotation and rescaling

CF MSE

Not rotation and scaling 0.5509 0.0206

With Rotation and rescaling

0.5081 0.0301

Affected by lowpass filter

The watermarked image is blurred The extracted logo is equivalent to original log convolve with a low pass filter

Filter size 3 by 3 5 by 5

CF MSE CF MSE

Not Filtered Watermark

0.5660 0.0320 0.3340 0.0820

Filtered Watermark

0.8282 0.0206 0.7083 0.0471

Affected by JPEG lossy by JPEG lossy compressioncompression

Original size Resolution MSE CF

4.1MB 2k X 2k 0.1194 0.5130

Compressed size

Compression ratio

MSE CF

555KB 7 0.1385 0.4172

312KB 13 0.1562 0.3798

199KB 20 0.1901 0.3251

Affected by random noiseby random noise10% noise 20% noiseA zero mean random noise profile

Affected by noiseby noise

Noise level

MSE CF

10% 0.1264 0.4955

20% 0.1402 0.4576

50% 0.1917 0.3327

100% 0.2485 0.2143

50% noise 100% noise

Affected by Croppingby CroppingCropping MSE CF

50% 0.1973 0.4301

80% 0.2602 0.4173

90% 0.5949 0.2119

Multiple watermarksMultiple watermarks

With the same keyTwo watermarks embedded and extracted with different keys

Affected by halftone printingby halftone printing

Lena after printed and scanned Extracted watermark

Halftoning can destroy the correlation between Halftoning can destroy the correlation between image and watermark.image and watermark.

ConclusionsConclusions

• A phase dispersion carrier function is the key for the algorithm to work.

• α = 0.2 gives the best balance between visibility and signal strength.

• It can resist the following attacks: lowpass filtering, cropping, lossy compression, noise, rotation and rescaling.

• Very sensitive to rotation angles.• More work needed for handling halftoning.

What’s done so far?What’s done so far?

• Basic functionality: carrier function, embed and extract simple iconic image, binary message, make it invisible.

• Embed into multiple-tile images and make it robust.

• Blurring, cropping, noise, rotation, lossy compression and rescaling resistant.

• Performance evaluation.

Future workFuture work

• Deal with printing halftoning attacks

• Support color images, embed the hiding information in chromatic channels and keep the luminance unchanged.

• Deal with image distortion.

• Make it a stand alone application by integrate the Matlab code with C code

Thank youThank you

• Questions?