1. Presenting: Yossi Salomon Noa Reiter Guides: Dr. Ofer Hadar Mr. Ehud Gonen

2. Outline  Main  What is Watermark  Methods Comparison  Methods  Our improvement

3. Project description  Design and implement different types of watermark techniques.  Correlation based methods.  Modulo base method.  Compare the methods using different measurement.  Improve modulo method in order to minimize bandwidth.

4. What is Digital Watermark???   A technique which allows an individual to add hidden copyright notices or other verification messages to digital Image.   Watermark message is a group of bits describing information pertaining to the signal or to the author of the signal.

5. Outline  Main  What is Watermark  Methods Comparison  Methods  Our improvement

6. Measuring watermark quality Three main quality measurements exists:  Capacity – inserting as much data possible in a fixed capacity.  Robustness – resistance against different types of attacks, noises and etc…  Imperceptibility – watermark must be imperceptible to human eyes.  Tradeoff between the three measurements. Robustness Capacity Imperceptibility

7. How to compare different methods???  It is very important to select a variety of measurements representing different aspects of watermark technique.  Each measurement must be held on the same watermark and image.  Each attack will operate the same way on each method.

8. Measurements  PSNR : This is the most critical measurement, it examines the damage done to the image by embedding the Watermark.  Watermark recovery: Measures the watermark total robustness, high Watermark recover ability will insure the success of the method.  Robustness against Noise: Pictures are often transmitted through communication channels, we would like to simulate this noisy channels.  Robustness against Attacks: Preventing a third side from achieving or damaging the Watermark at the lowest possible redundancy.

9. Outline  Main  What is Watermark  Methods Comparison  Methods  Our improvement

10. Correlation Based Methods  Many methods uses the correlation tool properties in order to embed and extract watermarks.   Correlation is a statistical tool which measures the amount of similarity between two parameters or random process.

11. Modulo Based Method  This method created and improved by Dr. ofer hadar team, and called: High Capacity Data Embedding In JPEG Bit Stream  Most images transmitted over the web are compressed using JPEG.  First we need to understand what is JPEG.

12. JPEG compression: ZIGZAG QDCT Entropy Coding

13.  We will change the coefficients in order to achieve sum mod2^n equals to watermark pixel value.  The main idea of the method is changing coefficients while minimizing image distortion.  Selecting the coefficients that will be changed in order to insert the watermark is effected by three main criteria's. Modulo Method criteria's

14.  In jpeg compression, each coefficient will be rounded to it’s closest integer.  For example coefficients 6.51 and 6.99 will both be rounded up to 7,although changing 6.51 value to 6 after quantization will create a lower distortion than changing 6.99.  Using this idea, we will change coefficients closer to the middle. Half way close criteria

15.  Different coefficients influence image quality differently.  For example, changing low level coefficient may critically damage image quality.  While selecting coefficients we must consider coefficients magnitude.  Each coefficients will have a magnitude weight factor. Magnitude of the DCT coefficient criteria

16. Position of the DCT coefficients criteria  Changing low level coefficients will create visible distortion.  Changing high level coefficients will damage watermark robustness (LPF for exp)  We will prefer to change Middle level coefficients.

17. Outline  Main  What is Watermark  Methods Comparison  Methods  Our improvement

18. Minimizing Bandwidth  JPEG stream final stage is entropy coding. In this stage each coefficient is coded using run level table.  Code length decrease monotonically, as function of level.  Our goal is to reduce the length with distortion restriction.

19. Minimizing Bandwidth (cont)  Tradeoff :  Distortion – changing more coefficients or changing coefficient farer from the middle will create a bigger distortion.  Bandwidth (Rate) – choosing the round down possibility will lead to level dropping and reduce bandwidth.  Number of Max coefficients selected:

20. Minimizing Bandwidth (cont)   Cost Function is built in order to combine the two main parameters, Distortion and Rate. selecting coefficients can be formulated in the following way: Distortion between original block and new coefficient chosen. Number of Bits allocated for Frame. Number of Bits for our selected coefficients.

21. Lagrange theorem: Lagrange theorem:  Using rate as a fixed restriction, we can use Lagrange theorem to transform our cost function into an equation without restriction. big means that more weight is given to the Rate parameter.  Calculating optimal rate and distortion using different will lead to converging into our optimal solution.