1. Digital Watermarking With Phase Dispersion Algorithm Team 1 Final Presentation SIMG 786 Advanced Digital Image Processing Mahdi Nezamabadi, Chengmeng Liu, Michael Su

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

3. 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

4. Digital Watermarking With Phase Dispersion 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.

5. Malicious 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

6. Embedding process illustration

7. Watermark extraction process

8. Indices for image difference MSE (Mean square error) Correlation factor

9. 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

10. Implementation of Binary Message template 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

11. Implementation of Binary Message template 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

12. Rotation/Scale Detection Thresholding

13. Rotation/Scale Detection Image rotation

14. Recovering image from distorted image

15. How it works? For example: In matrix form it will be:

16. Matirx form 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.

17. Matrix form continued…

18. Apply matrix A and some extra simple 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.

19. Rotation and rescaling CFMSE Not rotation and scaling0.55090.0206 With Rotation and rescaling 0.50810.0301

20. Affected by lowpass filter The watermarked image is blurred The extracted logo is equivalent to original log convolve with a low pass filter Filter size3 by 35 by 5 CFMSECFMSE Not Filtered Watermark 0.56600.03200.33400.0820 Filtered Watermark 0.82820.02060.70830.0471

21. by JPEG lossy compression Affected by JPEG lossy compression Original sizeResolutionMSECF 4.1MB2k X 2k0.11940.5130 Compressed size Compression ratio MSECF 555KB70.13850.4172 312KB130.15620.3798 199KB200.19010.3251

22. by random noise Affected by random noise 10% noise20% noiseA zero mean random noise profile

23. by noise Affected by noise Noise level MSECF 10%0.12640.4955 20%0.14020.4576 50%0.19170.3327 100%0.24850.2143 50% noise 100% noise

24. by Cropping Affected by Cropping CroppingMSECF 50%0.19730.4301 80%0.26020.4173 90%0.59490.2119

25. Multiple watermarks With the same key Two watermarks embedded and extracted with different keys

26. by halftone printing Affected by halftone printing Lena after printed and scanned Extracted watermark Halftoning can destroy the correlation between image and watermark.

27. Conclusions 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.

28. 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.

29. Future 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

30. Thank you Questions?