1. Ikhwannul Kholis Universitas 17 Agustus 1945 Jakarta
Digital Watermarking
Ikhwannul Kholis
Universitas 17 Agustus 1945 Jakarta

2. Agenda Background Terminology Applications Techniques Research topics
References

3. Information Hiding Information Hiding…..started with
Steganography (art of hidden writing):
The art and science of writing hidden messages in such a way that no one apart from the intended recipient knows of the existence of the message. The existence of information is secret.
Stego – Hidden , Graphy – Writing  ‘art of hidden writing’

4. Steganography (dates back to 440 BC)
Histaeus used his slaves (information tattooed on a slave’s shaved head )
Initial Applications of information hiding  Passing Secret messages

5. Microchip - Application
Germans used Microchips in World War II
Initial Applications of information hiding  Passing Secret messages

6. What is a watermark ?
What is a watermark ? A distinguishing mark impressed on paper during manufacture; visible when paper is held up to the light (e.g. $ Bill)
Application for print media  authenticity of print media

7. What is a watermark ?
Digital Watermarking: Application of Information hiding (Hiding Watermarks in digital Media, such as images)
Digital Watermarking can be ?
- Perceptible (e.g. author information in .doc)
- Imperceptible (e.g. author information in images)
Visibility is application dependent
Invisible watermarks are preferred ?

8. Applications Copyright Protecton:To prove the ownership
of digital media
Eg. Cut paste of images
Hidden Watermarks represent the copyright information

9. Applications Tamper proofing: To find out if data was tampered.
Eg. Change meaning of images
Hidden Watermarks track change in meaning
Issues: Accuracy of detection

10. Applications Quality Assessment: Degradation of Visual Quality
Loss of Visual Quality
Hidden Watermarks track change in visual quality

11. Comparison Watermarking Vs Cryptography
Watermark D  Hide information in D
Encrypt D  Change form of D

12. Watermarking Process
Data (D), Watermark (W), Stego Key (K), Watermarked Data (Dw)
Embed (D, W, K) = Dw
Extract (Dw) = W’ and compare with W
(e.g. find the linear correlation and compare it to a threshold)
Q. How do we make this system secure ?
A. K is secret (Use cryptography to make information hidden more secure)

13. Watermarking Process Example – Embedding (Dw = D + W)
Matrix representation (12 blocks – 3 x 4 matrix)
(Algorithm Used: Random number generator RNG), Seed for RNG = K, D = Matrix representation, W = Author’s name 1 2 3 4 5 6 7 8 9 10 11 12

14. Watermarking Process Example – Extraction
The Watermark can be identified by generating the random numbers using the seed K 1 6 8 10

15. Data Domain Categorization
Spatial Watermarking
Direct usage of data to embed and extract Watermark
e.g. voltage values for audio data
Transform Based Watermarking
Conversion of data to another format to embed and extract.
e.g. Conversion to polar co-ordinate systems of 3D models, makes it robust against scaling

16. Extraction Categorization
Informed (Private)
Extract using {D, K, W}
Semi - Blind (Semi-Private)
Extract using {K, W}
Blind (Public)
Extract using {K}
- Blind (requires less information storage)
- Informed techniques are more robust to tampering

17. Robustness Categorization
Fragile (for tamper proofing e.g. losing watermark implies tampering)
Semi-Fragile (robust against user level operations, e.g. image compression)
Robust (against adversary based attack, e.g. noise addition to images)
This categorization is application dependent

18. Categorization of Watermark
Eg1. Robust Private Spatial Watermarks
Eg2. Blind Fragile DCT based Watermarks
Eg3. Blind Semi-fragile Spatial Watermarks

19. Watermarking Example Application: Copyright Protection
Design Requirements:
- Imperceptibility
- Capacity
- Robustness
- Security

20. Imperceptibility Watermarking Stanford Bunny 3D Model
Visible Watermarks in Bunny Model  Distortion
Watermarking
Invisible Watermarks in Bunny Model  Minimal Distortion
Stanford Bunny 3D Model

21. Robustness Adversaries can attack the data set and
remove the watermark.
Attacks are generally data dependent
e.g. Compression that adds noise can be used as an attack to remove the watermark. Different data types can have different compression schemes.

22. Robustness Value Change Attacks
- Noise addition e.g. lossy compression
- Uniform Affine Transformation e.g. 3D
model being rotated in 3D space OR
image being scaled
If encoding of watermarks are data value dependent 
Watermark is lost  Extraction process fails

23. Robustness Sample loss Attacks - Cropping e.g. Cropping in images
- Smoothing e.g. smoothing of audio
signals e.g. Change in Sample rates
in audio data change in sampling rat
results in loss of samples
If watermarks are encoded in parts of data set which are
lost  Watermark is lost  Extraction process fails

24. Robustness Reorder Attack
- Reversal of sequence of data values e.g. reverse filter in audio signal reverses the order of data values in time 1 1 1 1
Attack 1 2 3 3 2 1
Samples in time
Samples in time
If encoding is dependent on an order and the order is changed  Watermark is lost Extraction process fails

25. Capacity Multiple Watermarks can be supported.
More capacity implies more robustness since watermarks can be replicated.
Spatial Methods are have higher capacity than transform techniques ?

26. Security
In case the key used during watermark is lost anyone can read the watermark and remove it.
In case the watermark is public, it can be encoded and copyright information is lost.

27. Watermarking Algorithm Design Requirements
As much information (watermarks) as possible
 Capacity
Only be accessible by authorized parties
 Security
Resistance against hostile/user dependent changes
 Robustness
Invisibility
 Imperceptibility

28. Tamper proofing
Robustness against user related operations – compression, format conversion
Accuracy of Detection – Only changes in meaning should be detected

29. References http://en.wikipedia.org/wiki/Steganography
THANK YOU !