digital watermarking parag agarwal {[email protected]}
TRANSCRIPT
Agenda
• Background
• Terminology
• Applications
• Techniques
• Research topics
• References
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’
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
Microchip - Application
• Germans used Microchips in World War II
Initial Applications of information hiding Passing Secret messages
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
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 ?
Applications
Copyright Protecton:To prove the ownership
of digital media
Eg. Cut paste of images
Hidden Watermarks represent the copyright information
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
Applications
Quality Assessment: Degradation of Visual Quality
Loss of Visual Quality
Hidden Watermarks track change in visual quality
Comparison
• Watermarking Vs Cryptography
Watermark D Hide information in D
Encrypt D Change form of D
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)
Watermarking ProcessExample – 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
Watermarking ProcessExample – Extraction
• The Watermark can be identified by generating the random numbers using the seed K
1
6
10
8
Data Domain Categorization
• Spatial WatermarkingDirect usage of data to embed and extract Watermark
e.g. voltage values for audio data
• Transform Based WatermarkingConversion 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
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
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
Categorization of Watermark
Eg1. Robust Private Spatial Watermarks
Eg2. Blind Fragile DCT based Watermarks
Eg3. Blind Semi-fragile Spatial Watermarks
Watermarking Example
Application: Copyright Protection
Design Requirements:
- Imperceptibility
- Capacity
- Robustness
- Security
Imperceptibility
Stanford Bunny 3D Model Visible Watermarks in Bunny Model Distortion
Watermarking
Stanford Bunny 3D Model
Watermarking
Invisible Watermarks in Bunny Model Minimal Distortion
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.
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
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
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 123Attack
0 1 1 1 1 0
Samples in time Samples in time
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 ?
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.
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
Tamper proofing
• Robustness against user related operations – compression, format conversion
• Accuracy of Detection – Only changes in meaning should be detected
References
• http://en.wikipedia.org/wiki/Steganography
• http://en.wikipedia.org/wiki/Digital_watermark
• http://www.cypak.com/pictures/med/Cypak%20microchip.jpg
THANK YOU !