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 ?
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
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
5
2 43
6
10
7
11 12
8
9
14. Watermarking Process
Example – Extraction
• The Watermark can be identified by generating the
random numbers using the seed K
1
6
10
8
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
20. Imperceptibility
Stanford Bunny 3D Model Visible Watermarks in
Bunny Model Distortion
Watermarking
Stanford Bunny 3D Model
Watermarking
Invisible Watermarks in Bunny
Model Minimal Distortion
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
If encoding is dependent on an order and the order is changed
Watermark is lost Extraction process fails
1 2 3 123
Attack
0 1 1 1 1 0
Samples in time Samples in time
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
