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

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 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

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

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

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

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 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

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 !