Robust watermarking technique sppt

ROBUST WATERMARKING
TECHNIQUES
FOR COLOR IMAGES

Presented By

Amit Phadikar

1

Introduction

Technologies for Security of Multimedia Data
Fingerprinting
Cryptography
Stegnography
Watermarking

2

Difference among Fingerprinting,
Cryptography, Steganography
and Watermarking
Fingerprinting uses some kind of hash functions
to create fingerprint, original file remain intact.
Cryptography is about protecting the meaning of
the document.
Steganography is about concealing their very
existence.
Watermarking is about robustness against
possible attacks, Watermark need not be hidden.
3

Watermarking can be applied to
Images
Text
Audio
S/W

Digital Image watermarking
A Digital Signal or pattern inserted into a digital
image.
4

General Framework for Watermarking
Encoding Process

E(I,S)= Î

5

Decoding Process

D(J,I)= SI

6

Applications of image
watermarking
IPR Protection
Demonstration of rightful
ownership
Authentication
Labeling for data retrieval
Covert communication

8

Properties of Digital Watermark
Perceptually invisible
Robustness
Cost
Capacity
Recoverable
Reversible
Undetectable
Able to determine the true owner
High bit rate

9

Attacks on Digital Watermarking

Lossy Compression
Geometric Distortions
Common Signal Processing Operations
– Linear filtering such as high pass
and low pass filtering
– Non-linear filtering such as median
filtering
– Addition of a constant offset to the
pixel values
– Addition of Gaussian and Non
Gaussian noise
– Local exchange of pixels
Jitter Attack
10

Work Already Done On This
Field
Watermarking domain
Frequency domain
Spatial domain

Frequency domain
Watermark is embedded in DFT, DCT
and DWT domain coefficients

11

Representative work
Cox:- DCT
Watermark was a sequence of 1000 random numbers.
Watermark was embedded in the 1000 largest DCT
coefficients. Correlation based non-blind detection were
performed
Xia Boncelet:- DWT
Proposed to add Gaussian noise as watermark in the
middle and high frequency coefficient of DWT. Detection
was correlation based.
FM Boland:-DFT
Embedded the watermark in the phase information in the
discrete Fourier transform domain since the phase
distortion is more sensitive to HVS than magnitude
distortion . There fore it is more robust to tampering when
compared to magnitude distortion.
12

Spatial Domain
Schyndel, Tirkel, and Osborne :- LSB
Generated a watermark using an m-sequence generator.
The watermark was embedded to the LSB of the original
image. Cross-correlation based detection was proposed.
The watermark, however, was not robust to additive noise.

Bender :- Statistical
Described the patch work algorithm, it chooses randomly n pair of
image point (ai,bi) and increased the ai by one, while decreased the bi
by one. The watermark was detected by comparing the sum of the
difference of ai and bi of the n pairs of the points with 2n provided,
certain statistical propriety like image intensity are uniformly distributed.
The scheme is extremely sensitive to geometric transformation.

13

Spatial Domain
P.Bas, J. M. Chassery and B.Macq :- Feature based
Proposed to find feature points and apply Delaunay tessellation to obtain
the triangular sequence. The watermark is right-angled isosceles
triangular sequence generated from a random sequence depending on a
secret key. After applying affined transform and visual mask watermark
sequence is added to the image. Detection is performed by finding
Delaunay tessellation of the test image and wiener filtering to obtain
watermark and then performing correlation.
Ping Wah Wong and Nasir Memon :- Block based
Proposed partitioning both host and binary watermark Image into blocks,
setting LSB’s of each image block to zero, applying hash function (MD5)
to image block. The watermark image block is ex-ored with the output of
the hash function and output is inserted into the LSB of the image block
to form watermarked image block. For extraction reverse steps are
followed. Scheme is reported to detect and report any changes to the
image.
14

Convolution Encoding and Decoding
In a general rate R = b/c, b<= c binary convolution encoder (time-invariant and
without feedback) the causal information sequence
u = u0u1 ………………= u0(0) u0(1) ……. u0(b) u1(0) u1(1)………. u1(b)……..
is encoded as the causal code sequence
v= v0 v1 ………………= v0(0) v0(1) ……. v0(c) v1(0) v1(1)………. v1(c)………..
Where vt = f(ut, ut – 1………….ut - B):

The function f must be a linear function. Furthermore, the parameter B is called the
encoder memory.
u = u0u1 ……..

v t = ut G 0 + ut- 1G 1 +……. + ut- BG B;

15

Viterbi Decoding
it estimateS v1 a sequence v that maximizes P(r/ v).
Where r is sequence , Probability p and
the starting and ending state is predetermined to be
the zero-state

Why Viterbi Decoding
•A highly satisfactory bit error performance,
•High speed of operation,
•Ease of implementation,
•Low cost.
•Fixed decoding time.

16

Quad Tree Region Splitting Image Segmentation
Method

Region Based Image Segmentation
Let R represent the entire image region, then we may view region
based segmentation as a process that partitions R into n sub
regions, R1,R2……..Rn, such that
n
(a) U Ri=R. (b) Ri is a connected region, i=1,2………n.
i=1
(c) Ri ∩Rj=Φ for all i and j, i≠j. (d) P (Ri)=TRUE for i=1,2….n.

17

Quad Tree Approach

Quad trees

Advantage of Quad Tree Decomposition
Small regions represent the presence of critical information of the image and hence
are the good place for the watermark insertion

18

Spatial Domain Watermarking

watermark insertion 19

watermark detection
20

Watermark Insertion Algorithm
Apply QUAD TREE decomposition on color image I (x, y) and select all 4x4 blocks
in blue channels.

Watermark embedding region

Repeat (for each selected 4x4 block (H) of blue channel)
{ Step 1: Compute the average, Imean, minimum, Imin, and
maximum, Imax, of the the pixels in H.
Step 2: Classify each pixel into one of two categories, based
on whether its intensity value is above or below the mean intensity of the block,
i.e., the ijth pixel, bitij is classified depending on its intensity, I, as
bitij ∈ YH if I >Imean
bitij ∈ YL if I ≤ Imea
whereYH and YL are the high and low intensity classes, respectively.
21

Step3: Compute the means, meanL and meanH, for the two
classes, YL and YH.
Step 4: Define the contrast value of block H as
CB = max(Cmin, β(Imax-Imin))
where β is a constant and Cmin is a constant which defines the minimal
value a pixel's Intensity can be modified.
Step5: Select a watermark bit (bitw ) randomly depending
on the key value.

Step 6: Given the value of bitw is 0 or 1, modify the pixels in H according to:
if bitw = 1,
I new = Imax + λ if I > meanH
I new =Imean + λ if meanL ≤ I < Imean
I new =I + δ otherwise
if bitw = 0,
I new = Imin - λ if I < meanL
I new = Imean - λ if Imean ≤I < meanH
I new = I - δ otherwise
Where I new is the new intensity value for the pixel which had original intensity
value I and δ is a random value between 0 and CB and λ is the watermark
strength.
Step 7: The modified block of pixels, Hnew, is then positioned the watermark
image in the same location as the block, H, of pixels from the original host
image.
} Until all watermark bits are inserted.
22
Step 8: Marge red, green and blue channel.

Watermark Extraction Algorithm

Apply QUAD TREE decomposition on original image I (x, y) and select all 4x4
blocks in blue channels that passes the homogeneity test, and whose all pixel
coordinate (X, Y) values lies in the range Xmin +(Xmax- Xmin )/4 <=X<=Xmax -
(Xmax- Xmin )/4 and Ymin +(Ymax- Ymin )/4 <=Y<=Ymax - (Ymax- Ymin )/4 where
Xmin, Xmax, Ymin, Ymax are the minimum , maximum coordinate value in X and Y-
axis of that image.

Repeat{
Step 1: take one 4x4 blocks of the host image and
Corresponding 4x4 block of watermarked Image using the same coordinate value as
of 4x4 block of host image .
Step2: a watermark bit is decoded by making the comparison of the two
resultant values:
If Average w > Averageo, then bit w = 1
If Average w ≤ Average o, then bit w = 0
Where Average o and Average w are the averages for the 4x4 blocks of the host
and Corresponding 4x4 block of watermarked Images, respectively.
} Until all watermark bit are extracted.

The decoded bits are then arranged in order using same key, which was used during
embedding. Then, the encoded watermark is exclusive ored by 128 bit key and then
decoded by viterbi decoding. 23

Results:
Cmin =15, β=1 and λ=15 and convolution encoding rate R=1/2. Test
Image LENA.BMP (512X512) ,Watermark is a 50x50 binary bitmap

NCC =∑∑ W ij W' ij / ∑∑Wij ] 2

ij ij

(a) (b) (c) (d)

Fig (a) original or host image (b) watermark image (c)
watermarked images (d) Extracted watermark
24

PSNR VS λ

45
44

PSNR
43
42
41
40
0 5 10 15 20

λ

(e) (f)

Fig (e) watermark embedded region. (f) the variation of PSNR w.r.t. Various values of
λ.

Wiener filtered watermarked image Median filtered watermarked image
and extracted watermark ,mask(3x3) and extracted watermark,mask(3x3)
25

Scaled down watermarked image, rescaled image and extracted watermark, scale
down factor=.75

Cropped watermarked image and extracted Jpeg compressed watermarked image
watermark , mask(444x444) and extracted watermark
26

Rotated watermarked image, rotation corrected image and extracted
watermark,angle=-17 degree
Table Showing the Normalized Cross correlation values for different operations

Table lists the PSNR between original host image and watermarked image for various
value of λ

27

Graphs for different operations showing variation of NCC value against various factors

28

Wavelet Domain Watermarking
Discrete wavelet Transform (DWT)
The DWT and IDWT can be mathematically stated as follows

A signal, x [n] can be decomposed recursively as
and

the signal x [n] can be reconstructed from its DWT coefficients recursively

To ensure the IDWT and DWT relationship, the following orthogonality condition on
the filters and

29

Figure. DWT pyramid decomposition of an image. 30

Figure. Examples of a DWT pyramid decomposition 31

watermark detection
33

Watermark Insertion Algorithm
The host image B (x, y), which is used to embed a watermark is segmented by
quad tree decomposition to select all 4x4 blocks

Watermark embedding region

B'= [b1, b2…..bM ] = Quadtree (B,T)
T=20 is the threshold value used by Quadtree and bi denote ith block such that
1=<i<=M and function Quadtree is used for quad tree decomposition of image in
spatial domain.

34

The bit embedding strategy is as follows.
Repeat (for blue component of each selected 4x4 block bi Є B')
{1: perform single scale wavelet transform of block bi .
2: compute average(C avg ) of all coefficient Ci found in step 1.
C avg = 1/16 Σ Ci

3: for all coefficients CH Є Ci such that CH > C avg
4: Select a watermark bit (bitw ) randomly depending on the key
(k2) value from watermark bit sequence (W''' 2N ).
5: Given the value of bitw is 0 or 1, modify all the coefficients ch Є
CH according to:
if bitw = 1,
c'h = ch + λ* ch
if bitw = 0,
c'h = ch - λ* ch
Where c'h is the new value of coefficient in CH which had original Coefficient
value of ch and λ is the watermark strength
6: perform inverse single scale wavelet transform after
modification of Coefficient to get modified block b'i.
7: The original block of pixels bi is then replaced by b'i
} Until all watermark bits are inserted.
8: Marge red, green and blue channel to get the watermarked
image

35

Watermark Extraction Algorithm
Apply quad tree decomposition on original image B (x, y) and select all 4x4 blocks
that passes the homogeneity test, and whose all pixels (X, Y) lies in the range Xmin
+(Xmax- Xmin )/4 <= X <= Xmax - (Xmax- Xmin )/4 and Ymin +(Ymax- Ymin )/4 <= Y
<=Ymax - (Ymax- Ymin )/4 where Xmin, Xmax, Ymin, Ymax are the minimum ,
maximum coordinate value in X and Y axis of the image

Repeat {
1: Take one 4x4 blocks (bi) of the host image and Corresponding 4x4 block (b'i) of
watermarked Image using the same coordinate value as of 4x4 block of host
image .
2: perform single level wavelet transform of block bi and b'i to get the Coefficient in
wavelet domain which is defined as
Ci = WT(bi)
C'i = WT(b'i),
where WT denotes single level wavelet transform. Ci and C'i are the vectors of the
same length representing wavelet coefficient
3: compute average of all coefficient C avg of Ci .
4: initialize variable sum_w=0 and sum_o =0;
5: for j=1:16
if Ci (j) > C avg
sum_w = sum_w + C'i (j);
sum_o = sum_o + Ci (j);
end
end 36

6: a watermark bit ( bit w) is decoded by making the
comparison
if sum_w < sum_o, then bit w = 0
if sum_w >= sum_o, bit w = 1
7: find original position (pos) of extracted watermark bit (
bitw ) using Key (k2) as seed.
8: W2 [pos]= bit w . Where W2 is an array for storing
extracted Watermark bits.
} Until all watermark bit are extracted.

Then, the encoded watermark is exclusive ored by 128 bit key and then decoded by
viterbi decoding.

37

Results: λ=.3 and convolution encoding rate R=1/2. Test Image kids.tif (512X512)
,Watermark is a 50x50 binary bitmap

(a) (b) (c) (d)
PSNR VS λ

60
40

PSNR
20
0
0 0.2 0.4 0.6
λ

(e) (f)
Fig (a) original or host image (b) watermark image (c) watermarked images
(d) Extracted watermark (e) watermark embedded region. (f) the variation of
PSNR w.r.t. various values of λ.
38

Wiener filtered watermarked image Median filtered watermarked image
and extracted watermark,mask(3x3) and extracted watermark,mask(3x3)

Scaled down watermarked image, rescaled image and extracted watermark,scale down
factor=.75

39

Cropped watermarked image and extracted watermark ,mask(444x444)

Jpeg compressed watermarked image and extracted watermark

40

Rotated watermarked image, rotation corrected image and extracted
watermark,angle=-10 degree
Table Showing the Normalized Cross correlation values for different operations

Table lists the PSNR between original host image and watermarked image for various
value of λ

41

Wiener Filter Median Filter Scale Dow n

1.5 1.05
1.5
1 1

NCC
1

NCC
NCC

0.5 0.5 0.95
0 0.9
0
0 5 10 0 0.5 1
0 5 10
Mask Size(N X N) Mask Size(N XN) Scale Dow n Factor

Scale Up Rotation in Positive Direction Rotation in Negative Direction
1.5 1.5 1.5
1 1 1

NCC

NCC
NCC

0.5 0.5 0.5
0 0 0
0 1 2 3 4 0 50 100 0 50 100
Scale Up Factor Angle in Degree Angle in Degree

Lossy JPEG Com pression
1.1
0.9
NCC

0.7
0.5
0 50 100 150
JPEG

Fig Graphs for different operations showing variation of NCC value against various
factors

42

Conclusion and Research Directions
Both the algorithms are robust to common image
processing operations, as shown by the results.
But none of the techniques proposed so far seems to be
robust to all possible attacks
Most of the current methods require the original image at
watermark recovery this may prove as serious limitation at
certain moments, as the image may not be accessible
Very few algorithms deal with the problems associated
with geometric changes.
Very few algorithms proposed in the literature consistently
survive the random bending attacks.
Bulk of the literature contains linear additive watermarks,
few algorithms resist the watermark copy attack and
ambiguity attack.
Consequently further work needs to be done to improve
the robustness of algorithms. 43

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Publication
Paper accepted

1) Spatial Domain Robust Image Watermarking Scheme, ADIT Journal of
Engineering, ISSN 0973-3663, pp 45-50, vol. 2,no. 1, Dec 2005.
Co-author(s): Bhupendra Verma, Sanjeev Jain

2) A New Color Image Watermarking Scheme, INFOCOMP Journal of Computer
Science (INFOCOMP, an International Journal), accepted for publication,
2006.
3) A spatial domain non-oblivious robust image-watermarking scheme, accepted
in International Conference on Information Security (ICIS), st
is tanbul, Turkey, 24-26 June

52

Papers Communicated
1) Spatial domain robust blind Watermarking scheme for color image, Image and
Vision Computing (International Journal, Publisher Elsevier)


2) Quad Tree Region Splitting Approach to Spatial Domain Robust Color Image
Watermarking, International Journal of Image and Graphics (IJIG), (Publisher
World Scientific).


3) Region Splitting Approach to Robust Color Image Watermarking In Wavelet
Domain, International Journal of Information Technology, (Publisher Computer
Society of Singapore).
53

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