1. Robustness Studies For a Multi-Mode Information Embedding Scheme for Digital Images Daniel Eliades Mentor: Dr. Neelu Sinha Department of Math and Computer Science, Fairleigh Dickinson University

2. Fairleigh Dickinson University Dainel Eliades2 Contents Introduction & Background of Digital Watermarking Overview of a Watermarking technique based upon Adaptive Segmentation and Space-Frequency Representation (WASSFR) Robustness Studies JPEG Compression

3. Fairleigh Dickinson University Dainel Eliades3 Introduction & Background Extensive use and distribution of digitized media in the Internet Age. Need for WASSFR to protect, detect and verify ownership of data. Affirmed by the US Digital Millennium Copyright Act (DCMA), enacted into law in October 1998.

4. Fairleigh Dickinson University Dainel Eliades4 Background Principles in the Design of a Watermarking Algorithm Imperceptibility Robustness/Redundancy Must be robust to signal processing distortions & attacks

5. Fairleigh Dickinson University Dainel Eliades5 Watermarking Scheme Watermark (W) Original Data (I) Key (k) Watermarked Data (I’) Watermark Insertion Digital Watermark

6. Fairleigh Dickinson University Dainel Eliades6 Watermarking Scheme Digital Watermark Watermark (W) or Original Data (I) Watermarked Data (I’) Key (k) Confidence Measure or Watermark (W) Watermark Detection

7. Fairleigh Dickinson University Dainel Eliades7 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.

8. Fairleigh Dickinson University Dainel Eliades8 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).

9. Fairleigh Dickinson University Dainel Eliades9 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

10. Fairleigh Dickinson University Dainel Eliades10 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

11. Fairleigh Dickinson University Dainel Eliades11 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

12. Fairleigh Dickinson University Dainel Eliades12 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.)

13. Fairleigh Dickinson University Dainel Eliades13 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 Metric Data Throughput vs. Robustness

14. Fairleigh Dickinson University Dainel Eliades14 Geometric attacksRemoval attacks Cryptographic attacksProtocol attacks We considered Jpeg compression Attacks

15. Fairleigh Dickinson University Dainel Eliades15 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.

16. Fairleigh Dickinson University Dainel Eliades16 256 x 256 Imperceptibility Test Original Image Watermarked Image

17. Fairleigh Dickinson University Dainel Eliades17 1024 x 1024 Imperceptibility Test Original Image Watermarked Image

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20. Fairleigh Dickinson University Dainel Eliades20 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

21. Fairleigh Dickinson University Dainel Eliades21 Digital Image Processing Rafael C. Gonzalez, Richard E. Woods & Steven L. Eddins Information Hiding–techniques for steganography and digital watermarking Stefan Katzenbeisser & Fabien A.P Petitcolas USC-SIPI Image Database http://sipi.usc.edu/database/ Jpeg Tutorial by Ray Wolfgang http://dynamo.ecn.purdue.edu/~ace/jpeg-tut/jpegtut1.html References