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Introduction to Watermarking Anna Ukovich Image Processing Laboratory (IPL)

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Presentation on theme: "Introduction to Watermarking Anna Ukovich Image Processing Laboratory (IPL)"— Presentation transcript:

1 Introduction to Watermarking Anna Ukovich Image Processing Laboratory (IPL)

2 The “Capture The Mark” contest  International contest on watermarking  It takes place on April 18 th, 2007, from 1.30 p.m. to 6 p.m.  Teams: Roma3, Trento, Siena, France, Finland, UK, Trieste?  1 st phase: hide the mark in the images  2 nd phase: attack other team marks

3 Outline  Introduction Purpose of watermarking Categories  Watermarking techniques overview  Example

4 Watermarking principles (I)  Digital watermarking technology allows users to embed data into digital contents such as text, still images, video and audio data.

5 Watermarking principles (II)  Embedded hidden information, which travels with the watermarked data, even after copying and redistribution.  How can information be hidden in digital data? Human perception is imperfect Make modification to the original data without changing its perceptual quality, exploit masking principle (JND).  Modifications can be detected via signal processing.

6 Purpose of watermarking  Copyright Protection Watermark can prove ownership in court  Fingerprinting To trace the source of illegal copies  Copy protection The information stored in a watermark can directly control digital recording devices for copy protection purposes  Data authentication  Tamper detection

7 Watermarking categories (I)  Visible  Invisible

8 Watermarking categories (II)  Robust: copyright protection Requirements: the watermark should be permanently intact to the host signal, removing the watermark result in destroying the perceptual quality of the signal.  Fragile: tamper detection Requirements: Break very easily under any modification of the host signal.  Semi Fragile: data authentication Requirements: Robust to some benign modifications, but brake very easily to other attacks.

9 Robustness example  Algorithm: Computer Vision Group, CUI, University of Geneva Watermarking domain: wavelet Encoding: Turbo codes Perceptual mask: multiresolution anisotropic Noise Visibility Function Message length: 64 bits

10 Robustness example: Drawing and Working

11 Robustness example: Tearing and Detection

12 Watermarking categories (III)  Readable watermark  Detectable watermark Marked image Decoder Marked image Yes/No Detector

13 Watermarking categories (IV): detection/extraction  Non-blind: use the original unmarked image  Semi-blind: does not use the original image but use some side information and/or the original watermark.  Blind: does not use the original image or any side information (most challenging).

14 Distortion and attacks  Image processing operations  Filtering, dithering, cropping, scaling, compression, etc.  Active attack  attacker attempts to remove or destroy the watermark  serious in proof of ownership, copy control, fingerprinting  Passive attack:  attacker tries to find if a watermark is present  serious for covert communication  Collusion attack:  attacker uses several copies of the watermarked data to construct a copy with no watermark  serious for fingerprinting applications  Forgery attack  attacker tries to embed a valid watermark  serious in authentication

15 Outline  Introduction Purpose of watermarking Categories  Watermarking techniques overview  Example

16 Watermarking techniques  Spatial domain watermarking Watermark embedded by directly modifying the pixel values.  Transform domain watermarking Watermark embedded in the transform domain e.g., DCT, DFT, wavelet by modifying the coefficients of global or block transform.

17 Least Significant Bit Techniques  Substitution Techniques: Substitute redundant parts of a cover with a secret message  Choose a subset of cover elements and substitute least significant bit(s) of each element by message bit(s)  Message may be encrypted or compressed before hiding  A pseudorandom number generator may be used to spread the secret message over the cover in a random manner Easy but vulnerable to corruption due to small changes in carrier

18 Example:LSB Encoding

19 Additive watermarking: Embedding  Add a weak signal (mark) representing ownership in host media The weak signal is known to detector Detection by correlating a test copy with the watermark signal  Invisibility: Watermark signals with structural patterns can be easily perceived than random noisy signals  Robustness: Watermarks added to perceptually insignificant components can easily be distorted

20 Additive watermarking: Detection

21 Detection: statistical decision theory  Detection is usually done by correlating the watermarked image with a locally generated version of the watermark at the receiver side  High correlation value when the watermark has been obtained with the proper key  Threshold selection: Neyman-Pearson criterion: given a fixed P F (probability of incorrectly deciding that an image has been watermarked with a certain key) the P D (probability of correctly deciding that a watermark is present) should be maximized

22 Spatial embedding example

23 Outline  Introduction Purpose of watermarking Categories  Watermarking techniques overview  Example

24 Initialization  read original image;  generate watermark vector of length N (e.g., N = 1000).  related function: imread, imshow, rand or randn

25 Embedding - 1  apply 2D DCT transform on the entire image;  find first N largest coefficients;  generate watermarked coefficients v' by v‘ = v * (1 + α * w) w is the corresponding watermark component, α is the scaling factor to control the strength of watermark (e.g., α = 0.1);

26 Embedding - 2  apply 2D IDCT and truncate pixel value to [0, 255] to obtain watermarked image;  display the marked image, visualize the difference between marked and unmarked image, check both visual quality and objective measure such as PSNR  related function: dct2, idct2, sort, imshow

27 Distortion  generate a distorted version of watermarked image.  the possible distortions are: JPEG compression, low pass filtering, resize, cropping, gaussian noise, quantization etc.  related function: imwrite, imread, filter2, imresize

28 Detection  image registration of the test image with respect to the original unmarked image is required before applying detection;  apply 2D DCT on test image;  identify the N largest coefficients, substract the corresponding value of the original unmarked ones, and compute the detection statistics with the watermark vector which is suspected to have been put in the test image  related function: corrcoef

29 Introduction to Watermarking Anna Ukovich Image Processing Laboratory (IPL)


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