Robustness Studies

For a Multi-Mode Information Embedding Scheme for Digital Images

Daniel EliadesMentor: Dr. Neelu Sinha

Department of Math and Computer Science, Fairleigh Dickinson University

Fairleigh Dickinson University Dainel Eliades 2

Contents

Introduction & Background of Digital Watermarking

Overview of a Watermarking technique based upon Adaptive Segmentation and Space-Frequency Representation (WASSFR)

Robustness Studies

JPEG Compression

Fairleigh Dickinson University Dainel Eliades 3

Introduction & Background

Extensive use and distribution of digitized media in the Internet Age.

Need for WASSFR to protect, detect and verifyownership of data.

Affirmed by the US Digital Millennium Copyright Act (DCMA), enacted into law in October 1998.

Fairleigh Dickinson University Dainel Eliades 4

Background

Principles in the Design of a Watermarking Algorithm

Imperceptibility

Robustness/Redundancy Must be robust to signal processing distortions

& attacks

Fairleigh Dickinson University Dainel Eliades 5

Watermarking Scheme

Watermark (W)

Original Data (I)

Key (k)

Watermarked Data (I’)

Watermark Insertion

Digital Watermark

Fairleigh Dickinson University Dainel Eliades 6

Watermarking Scheme

Digital Watermark

Watermark (W) or Original Data (I)

Watermarked Data (I’)

Key (k)

Confidence Measure or Watermark (W)

Watermark Detection

Fairleigh Dickinson University Dainel Eliades 7

Overview of WASSFR

Watermarking technique based upon Adaptive Segmentation and Space-Frequency representation

Need for WASSFR to protect, detect and verify ownership of data.

Extensive use and distribution of digitized media in the Internet Age.

Fairleigh Dickinson University Dainel Eliades 8

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

Selection of a suitable space-frequency representation for each segment To allow for highest watermark bit rate

Identification of perceptually most significant component in the transformed image

Insertion of the Watermark

Overview of WASSFR (cont).

Fairleigh Dickinson University Dainel Eliades 9

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

Uniform intensity or textured regions less affected by controlled noise injection in frequency domain

Edges less affected if noise profile is controllable in space domain

Perceptually significant components are easier to identify for a suitably segmented image

Adaptive Segmentation

Fairleigh Dickinson University Dainel Eliades 10

Instead of using pure frequency domain approach (as used by Cox et al.) use a set of space-frequency representations

Space representation If entropy <= T1

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

2-D Wavelet representation If T1 < entropy <= T2

Space-Frequency Representation

Fairleigh Dickinson University Dainel Eliades 11

Robustness/Bit error rate measurement

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

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

Robustness Studies

Fairleigh Dickinson University Dainel Eliades 12

Selection of an Image Database

Size of data and nature of data both have an impact on the robustness

Various classes of images used

Attacks

Geometric and removal attacks

Robustness Studies (cont.)

Fairleigh Dickinson University Dainel Eliades 13

Data Throughput – the number of embedded bits of information while keeping the perceptual distortion and detection ambiguity below desired thresholds.

A higher data throughput allows for better cryptography as well as powerful channel coding.

In practice, available data throughput tempered by the overhead required to achieve a desired level of robustness.

Performance MetricData Throughput vs.

Robustness

Fairleigh Dickinson University Dainel Eliades 14

Geometric attacks Removal attacks

Cryptographic attacks Protocol attacks

We considered Jpeg compression

Attacks

Fairleigh Dickinson University Dainel Eliades 15

Jpeg Compression

Jpeg (Joint Photographic Experts Group) uses a lossy compression technique which means that visual information is lost permanently.

Jpeg compression has four stages. Divides the image into 8x8 pixel blocks Calculates the Discrete Cosine Transform

(DCT) of each block A quantifier then rounds off the coefficients

according to the quantization matrix Final step is the binary encoder which

translates it to the data output stream.

Fairleigh Dickinson University Dainel Eliades 16

256 x 256 Imperceptibility Test

Original Image

Watermarked Image

Fairleigh Dickinson University Dainel Eliades 17

1024 x 1024 Imperceptibility Test

Original Image Watermarked Image

Fairleigh Dickinson University Dainel Eliades 18

256 x 256 Images

0

5

10

15

20

25

29.23% 38.46% 44.62% 56.92%

Jpeg Compression

To

tal N

um

ber

of

Pac

kets

Clock.tiffSeries2Airplane.tiffSeries4Chemical.tiffSeries6Walter02.tiffSeries8Walter04.tiffSeries10Walter06.tiffSeries12Walter08.tiffSeries14Walter10.tiffSeries16Walter12.tiffSeries18Walter14.tiffSeries20

Fairleigh Dickinson University Dainel Eliades 19

1024 x 1024 Images

0

50

100

150

200

250

300

350

400

34.73% 43.12% 48.49% 53.56% 58.34% 62.34%

Jpeg Compression

Nu

mb

er

of

Pa

ck

ets

AirportAirplaneMan

Fairleigh Dickinson University Dainel Eliades 20

A new Digital Rights Management System based on WASSFR was described.

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

Results quantify trade-offs between information throughput and robustness.

Conclusions

Fairleigh Dickinson University Dainel Eliades 21

Digital Image ProcessingRafael C. Gonzalez, Richard E. Woods & Steven L. Eddins

Information Hiding–techniques for steganography and digital watermarkingStefan Katzenbeisser & Fabien A.P Petitcolas

USC-SIPI Image Databasehttp://sipi.usc.edu/database/

Jpeg Tutorial by Ray Wolfganghttp://dynamo.ecn.purdue.edu/~ace/jpeg-tut/jpegtut1.html

References