10 Chapter 5

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CHAPTER 5

REVERSIBLE DATA HIDING BASED ON

HISTOGRAM SHIFTING

The reversible watermarking algorithms are developed from the

time it was suggested by its pioneers. Fridrich et al, Jun Tian and Ni et al are

pioneers in the field.

Ni et al (2003) proposed an image lossless data hiding algorithm

using pairs of zero-points and peak-points, in which the part of an image

histogram is shifted to embed data. Lossless data embedding algorithm based

on the histogram shifting in spatial domain is proposed. Fridrich suggested

general methodologies for lossless embedding that can be applied to images

as well as any other digital objects. The concept of lossless data embedding

can be used as a powerful tool to achieve a variety of necessary tasks,

including lossless authentication using fragile watermarks (Fridrich et al

2002).

Xuan et al (2005) proposed the lossless embedding using the

Integer Wavelet Transform (IWT) and histogram medication using a

threshold point for embedding limit. Xuan and Shi proposed a histogram

shifting method for image lossless data hiding in integer wavelet transform

domain. This algorithm hides data into wavelet coefficients of high frequency

subbands. It shifts part of the histogram of high frequency wavelet subbands

and embeds data by using the created histogram zero-point (Xuan et al 2006).

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Chrysochos et al’s (2007) scheme of reversible watermarking presents a

method resistant to geometrical attacks.

Fallahpour and Sedaaghi (2007) proposes relocation of zeroes and

peaks of the histogram of the image blocks of the original image to embed

data in the spatial domain. Image is divided into varying number of blocks as

required and the performance is analysed.

Zeng et al (2009) proposed scheme based on the difference

histogram shifting to make space for data hiding.

5.1 INTEGER -TO-INTEGER WAVELET TRANSFORMS

This algorithm again uses integer wavelet transform because the

algorithm proposed in this chapter based on histogram shifting is a reversible

algorithm.

In conventional wavelet transform reversibility is not achieved due

to the floating point wavelet coefficients we get after transformation. When

we take the inverse transform the original pixel values will get altered.

When we transform an image block consisting of integer-valued

pixels into wavelet domain using a floating-point wavelet transform and the

values of the wavelet coefficients are changed during watermark embedding,

the corresponding watermarked image block will not have integer values.

When we truncate the floating point values of the pixels, it may result in loss

of information and reversibility is lost. The original image cannot be

reconstructed from the watermarked image.

In conventional wavelet transform which is done as a floating-point

transform followed by a truncation or rounding it is impossible to represent

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transform coefficients accurately. Information will be potentially lost through

forward and inverse transforms.

In view of the above problems, an invertible integer-to-integer

wavelet transform based on lifting is used in the proposed scheme. It maps

integers to integers which are preserved in both forward and reverse

transforms. There is no loss of information. Wavelet or subband

decomposition associated with finite length filters is obtained by a finite

number of primal and dual lifting followed by scaling.

5.2 HISTOGRAM PROCESSING

The histogram of a digital image with gray levels in the range

(0, L-1) is a discrete function p(rk) = nk/n, where rk is the kth gray level, nk is

the number of pixels in the image with that gray level, n is the total number of

pixels in the image, and k = 0,1,2… L-1 (Gonzalez 2008).

An estimate of the probability of occurrence of gray level rk is

given by p(rk). A plot of this function for all the values of k provides a global

description of the appearance of the image. The gray levels are concentrated

toward the dark end of the gray scale range. Thus the histogram corresponds

to an image with overall dark characteristics. The opposite is that the

histogram which has a narrow shape indicates little dynamic range and thus

corresponds to an image having low contrast. As all gray levels occur toward

the middle of the gray scale, the image would appear a murkey gray. A

histogram with a significant spread, corresponds to an image with high

contrast.

The properties described are global description say nothing specific

about image content, the shape of the histogram of an image give us useful

information about the possibility for contrast enhancement. The following

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discussion develops methods for manipulating histograms in a consistent and

meaningful manner.

5.3 WAVELET HISTOGRAM SHIFTING

Integer Wavelet transforms of the original image is taken. In the

subband wavelet histogram data is to be embedded. In the histogram the

horizontal axis (X) represents the wavelet coefficients value and the vertical

axis (Y) represents the number of occurrence of the coefficients value. The

wavelet histogram normally exhibits a Laplacian distribution nature with a

peak point and sloping on either side. Peak in wavelet histogram is usually at

coefficient value ‘0’

Embedding can be done on both the sides of the histogram to get

the required embedding capacity. Data embedding is done by modifying some

of the coefficient values of the wavelet domain to it’s neighboring value by

shifting a portion of the histogram. This gives a good visual quality and

thereby a better PSNR between original image and watermarked image.

To embed data we choose the peak point of the histogram and call

it as P. Figure 5.1 shows a vacant point is created at Peak+1.This is done by

shifting all points with value Peak+1 and above one position to the right. Now

all the IWT coefficients are scanned and whenever a coefficient with value

peak is encountered, ‘0’ is embedded by leaving it as such and ‘1’ is

embedded by changing its value to peak+1.This is repeated till all the points

with value Peak are over. Then a new peak is created by shifting to the right

and data is embedded as per the algorithm. We choose the peak point so that

payload is maximized.

All the high frequency wavelet subbands can be utilized to get

maximum capacity. The same process can be done on the left side of the

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histogram Peak to embed more watermark bits. A reverse algorithm is applied

for extracting the watermark data.

After water mark bits are extracted, shifting is done towards the left

each time after data extraction so that the original coefficient values are

restored. This guarantees complete reversibility and the original image can be

exactly reconstructed without loss.

(a) (b)

Figure 5.1 Illustration of wavelet Histogram, (a) Maximum point is at

Peak, (b) Histogram with zero point created at peak +1

5.4 PROPOSED METHOD

5.4.1 Embedding Method

For the wavelet transformed image sub bands histogram is taken.

Now we can start embedding using the following steps. For the selected sub

band, set P = Peak of the histogram coefficients.

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Integer wavelet coefficient values

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Create a zero point at P+1 so that no point in the histogram has the

value P+1.To create the zero point shift all coefficients with value P+1 and

above to one position right. This makes P+1 as P+2, and the original P+2 to

P+3 and so on.

1. Peak = P; Select P and P+1 to embed data.

2. Read the n watermark bits Wb = {W0, W1, ...........Wn-1}where

0<b<n-1.

3. If Wb =0, then ‘0’ is embedded as P = P.

4. Else if Wb =1, then ‘1’ is embedded P = P+1.

5. Point P+1 gets slowly filled up depending upon the number of

Wb bits with value 1.

6. Go to histogram of the other sub bands to be marked and

repeat the same process.

7. While to- be- embedded watermark bits are still remaining, set

P=P+2 and go to step1.Otherwise stop.

Figure 5. 2 Embedding Method

Unmarked

Approximate

Coefficients Marked LH, HL

&HH

Coefficients

Preprocessing &Integer

Wavelet transform

Histogram Shifting and

Data Embedding

Algorithm

High frequency

sub bands Original Image

watermark

Watermarked

Image

Inverse IWT

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Figure 5.2 Shows the original image is decomposed into it’s sub

bands using integer wavelet transform After preprocessing IWT is used to

ensure complete reversibility. The high frequency sub bands (horizontal,

Vertical and Diagonal) are used for data embedding. Each sub band is used

one after the other to meet the required embedding capacity. Watermark bits

that forms the payload is embedded into these sub bands using the embedding

algorithm. The low frequency unmarked approximate coefficients are then

used along with the marked sub bands and Inverse IWT is taken to get the

watermarked image.

5.4.2 Extraction Method

Figure 5.3 Extraction Method

The extraction method is shown in Figure 5.3. Data extraction is the

reverse process. Integer wavelet Transform is taken for the watermarked

image. The watermarked high frequency sub bands are separated and using

the Data extraction algorithm, the watermark bits are retrieved and the

original sub bands are obtained. This is combined with the unmarked low

frequency sub band to get the original image. This method is completely blind

and reversible. Original image and the watermark data bits are obtained

without any loss.

Watermarked

ImageInteger Wavelet

transform

Histogram

Shifting & data

Recovery Algorithm

Inverse

IWT

Watermark

stream

High frequency

Subbands

Recovered original

Image

Approximate Low

frequency LL subband

Attack

identification

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After wavelet decomposition of the watermarked image, histograms

of the marked sub bands are taken. For the selected sub band, set Peak= Peak

of the histogram coefficients.

1. P=Peak. Read the coefficients with value P and

P+1.Whenever a coefficient with value P is read ,extract

watermark bit as Wb =0 and leave P unaltered. Whenever a

coefficient with value P+1 is read ,extract watermark bit as

Wb =1 and change P+1 to P.

2. Shift all the coefficients with value P+2 and above one

position to the left.

3. Go to histogram of the other marked sub bands and repeat the

same process.

4. Set P = P+1.

5. While all watermark bits Wn are not extracted go to

step1.Otherwise stop.

5.5 EXPERIMENTAL RESULTS AND DISCUSSION

Experiments are conducted using different 512 X 512 gray scale

images and different wavelets.

Various experiments are conducted to study the performance of the

embedding algorithm of histogram shifting. Experiments were done by using

different images, different wavelet decompositions on each image, by

embedding text of various sizes into the original image and by embedding

logo image of different sizes. The following sections illustrate the tests

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conducted and the results are tabulated. Tables 5.1 and 5.2 shows the peak

signal to noise ratio between the original image and the watermarked image

after embedding text of various sizes.

Table 5.1 shows Image Quality tested with gradual increase in

payload using Text Input of Different sizes on Cameraman Image.

Table 5.1 Image Quality tested with gradual increase in payload using

Text Input of different sizes on Cameraman Image

Number of Characters

EmbeddedPSNR (dB)

2486 48.7259

2974 48.6684

5027 48.4209

9126 47.6446

11291 47.3461

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Table 5.2 Comparison of performance of various wavelet families on a given image for different payload size

Cameraman

cdf2.2 db2 sym3 bior3.3 bior6.8 rbio3.3Pay

load bpp PSNR MSE PSNR MSE PSNR MSE PSNR MSE PSNR MSE PSNR MSE

10,000 48.965 0.844 44.63 2.308 43.05 3.296 46.86 1.374 39.937 6.806 43.507 2.992

15,129 48.951 0.847 44.628 2.311 43.05 3.299 46.82 1.386 39.934 6.811 43.498 2.998

25,281 48.869 0.864 44.609 2.321 43.02 3.318 46.76 1.403 40.010 6.813 43.478 3.011

50,176 48.751 0.887 44.567 2.344 42.94 3.357 46.62 1.450 39.945 6.846 43.468 3.019

75,076 48.787 0.880 insufficient insufficient 42.90 3.412 46.55 1.472 39.862 6.926 43.368 3.132

1,00,489 48.674 0.903 insufficient insufficient 42.84 3.465 insufficient insufficient insufficient insufficient insufficient insufficient

Insufficient - Indicates insufficient Capacity in the number of peaks used to embed.

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39

41

43

45

47

49

10000 30000 50000 70000 90000 110000

Payload in Bits

Imag

e Q

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lity

PS

NR

(dB

)

Cdf2.2

db2

Sym3

bior3.3

bior6.8

rbio3.3

Figure 5.4 Image Quality tested using different wavelets on

Cameraman Image

Table 5.2 shows image quality tested for different payloads on the

same image using different wavelets. Cdf2.2. performs better than other

wavelets for the same payload. Image quality quickly changes when different

wavelets are used. Performance in embedding, measured using peak signal to

noise ratio shows that bior6.8 has the minimum quality. The embedding

capacity also varies when using different wavelets using different wavelets

when the image is decomposed using db2 embedding stops in about 50,000

bits whereas cdf2.2 continues to embed over one lakhs bits. The same is

illustrated in the graph of Figure 5.4.

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db2 wavelet bior 3.3 wavelet

bior 6.8 wavelet rbio 3.3 wavelet

Figure 5.5 Cameraman wavelet Histograms using different Wavelets

Figure 5.5 illustrates the histograms of cameraman image

decomposed using different wavelets. The number of points in the peak value

of the wavelet histogram is different for each or decomposition. The more the

number of points in the wavelet histogram peak, the more is the embedding

capacity. The same is illustrated in Figure 5.5a to Figure 5.5d for db2, bior3.3,

bior6.8 and rbio3.3 respectively.


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Table 5.3 Embedding Capacity of various Images and their Quality

measured by varying number of Peaks used

Images

Cameraman Woman Dark

Hair

Lena Sail BoatNo of

Peaks

Used

Payload PSNR Payload PSNR Payload PSNR Payload PSNR

11 peaks 0.5506 41.8595 0.4954 42.2365 0.3940 38.0911 0.3748 37.7327

10 peaks 0.55 42.1261 0.4944 42.3553 0.3877 38.3574 0.3652 38.0314

9 peaks 0.5468 42.4130 0.4928 42.5261 0.3786 38.6859 0.3526 38.4327

8 peaks 0.5440 42.8195 0.4901 42.7120 0.3665 39.0803 0.3377 38.8818

7 peaks 0.5404 43.2402 0.4853 42.9094 0.3496 39.5308 0.3191 39.4865

6 peaks 0.5357 43.7392 0.4775 43.1588 0.3269 40.1725 0.2956 40.1670

5 peaks 0.5303 44.2951 0.4646 43.5402 0.2969 40.9678 0.2665 41.0522

4 peaks 0.5226 45.0178 0.4429 44.1540 0.2568 42.1267 0.2293 42.2822

3peaks 0.5012 45.8634 0.4038 45.0346 0.2057 43.6043 0.1831 43.9072

2 peaks 0.4675 47.0743 0.3264 46.5519 0.1443 45.9927 0.1281 46.2890

1 peak 0.3496 49.4225 0.1791 49.4425 0.0759 49.5160 0.0644 49.7468

Image quality is tested using different number of embedding points

in histogram to embed the watermark data. Table 5.3 shows the number of

bits embedded in the original image per pixel and shows the image quality

measured in PSNR for different grayscale images by number of peaks used in

the histogram for embedding data. Each coefficient value can embed

watermark bits equal to the number of occurrence of that point in the wavelet

histogram. Figure 5.6 shows image quality decreases as we use more and

more points in the histogram for embedding data. With lesser payload fewer

points are used and we get more image quality for the watermarked images.

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35

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39

41

43

45

47

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1 2 3 4 5 6 7 8 9 10 11

No. of Points in Histogram used for Embedding

mag

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SN

R (d

B)

Cameraman

Woman Dark Hair

Lena

Sail Boat

Figure 5.6 Image Quality vs number of Histogram points used for

Embedding

Table 5.4 Image Quality tested on Cameraman Image and number of

bits embedded and the number of shifting of the histogram

Shifting Capacity bits PSNR Payload

11 levels shifting 144337 41.8595 0.5506

10 levels shifting 143871 42.1260 0.5500

9 levels shifting 143328 42.4130 0.5468

8 levels shifting 142610 42.8195 0.5440

7 levels shifting 141665 43.2402 0.5404

6 levels shifting 140433 43.7392 0.5357

5 levels shifting 139021 44.2951 0.5303

4 levels shifting 136991 45.0178 0.5226

3 levels shifting 132932 45.8634 0.5012

2 levels shifting 122558 47.0743 0.4675

1 level shifting 91650 49.4225 0.3496

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CAMERA MAN

38

40

42

44

46

48

50

11 p

ks 0

.550

6

9 pk

s 0.

5468

7 pk

s 0.

5404

5 pk

s 0.

5303

3pks

0.5

012

1 pe

ak 0.

3496

No. of Pks Used (PayLoad)

ima

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SN

R d

B

PSNR

WOMAN DARK HAIR

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44

46

48

50

11 p

ks 0.

4954

9 pk

s 0.

4928

7 pk

s 0.

4853

5 pk

s 0.

4646

3pks

0.4

038

1 pk

0.1

791

No. of Pks (Pay Load)

Ima

ge

Qu

ali

ty P

SN

R d

B

PSNR

(a) (b)

Figure 5.7 Payload, Number of peaks used vs Image Quality

(a) Cameraman Image (b) Woman Dark hair Image

Table 5.5 Image Quality tested on Woman Dark Hair Image and

number of bits embedded and the number of shifting of the

histogram

ns Shifting Capacity PSNR Payload

11 11 levels shifting 129870 42.2365 0.4954

10 10 levels shifting 129617 42.3553 0.4944

9 9 levels shifting 129192 42.5261 0.4928

8 8 levels shifting 128472 42.7120 0.4901

7 7 levels shifting 127215 42.9094 0.4853

6 6 levels shifting 125181 43.1588 0.4775

5 5 levels shifting 121805 43.5402 0.4646

4 4 levels shifting 116100 44.1540 0.4429

3 3 levels shifting 105852 45.0346 0.4038

2 2 levels shifting 85575 46.5519 0.3264

1 1 level shifting 46942 49.4425 0.1791

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Table 5.4 and 5.5 illustrates how the number of watermark bits that

could be embedded in a given image for cdf 2.2 wavelet using a number of

embedding points. To embed in a peak we shift all the histogram points one

position to create a vacant point near the peak. As we increase the embedding

points the levels of shifting increase. Figure 5.7 (a) and 5.7(b) shows the

number of bits that can be embedded in the cameraman image and the woman

dark hair image respectively using one to eleven levels of shifting. Also the

signal quality of the images is shown. Also it illustrates the payload, number

of peaks used against the image quality for cameraman and woman dark hair

image respectively.

Figure 5.8 Watermarked Images marked at 0.4bpp (a) Sail Boat PSNR

37.05 dB, (b) Woman Dark Hair PSNR 45.03 dB,

(c) Camera Man PSNR 48.65 dB, (d) Lena PSNR 37.89 dB,

(e) Jet Plane PSNR 42.35 dB, (f) Lake PSNR 36.42 dB.

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Figure 5.8 illustrates the watermarked images of Sailboat, Woman

darkhair, Cameraman, Lena, Jetplane and lake images. For the same number

of bits embedded different images show different quality measured in PSNR.

The maximum quality is for cameraman image at 48.65 dB for 0.4 bits per

pixel ,woman dark hair image with 45.03 dB and minimum for Lake image

with 36.42 dB.

Table 5.6 Image quality tested for different images on various wavelets

for a fixed payload of 25,000 bits

Lena Boat CameramanWoman dark

hairWavelet

namePSNR PSNR PSNR PSNR

cdf2.2 46.3435 46.5839 48.8746 47.5951

db2 44.6218 44.9846 44.6034 44.8392

db3 42.3504 42.5154 42.1412 42.4439

coif1 41.4483

(16900bitsonly)

41.6768

(15625bits)

42.4858

(14884bits)

43.1489

(17689bits)

sym2 44.6218 44.9846 44.6034 44.8392

sym3 41.1251 41.1670 43.0265 42.5173

bior3.3 45.3731 45.5399 46.7578 46.3826

bior6.8 39.8899 40.4009 39.9917 40.2478

rbio3.3 41.0944 41.3254 43.5110 42.8716

rbio5.5 38.0903 (6889bits) 37.8837

(8100bits)

39.1146

(2401bits)

39.1939

(2809bits)

rbio6.8 40.4545 40.7971 40.4842 40.6646

Experiments were conducted on various 512 512 grayscale images

to study the performance of various wavelets on the embedding algorithm.

For a fixed payload of 25,000 bits embedded and tested, db1 performs best as

shown in the Table 5.6. Bior6.8 has the minimum quality. A variation of

about 10db in Peak signal to noise ratio exists while changing the wavelet

family used for decomposing the original image for embedding. Also PSNR

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between the original image and the watermarked image varies depending on

the image when using the same wavelet for decomposition. Histogram of

images for wavelet families which could not embed given capacity, tested for

25,000 bits is shown in the following figures. Histograms of some of the

images using coif 1 wavelet is shown in Figure 5.9(a) and (b).Cameraman

image when decomposed using coif 1 wavelet has its peak near 7000 in the

wavelet histogram of the horizontal wavelet coefficients, near 12000 in the

vertical coefficients and very less in the diagonal component. So the

decomposition using coif 1 is not able to embed the desired 25,000 bits.

Similar is the case of the boat image using coif 1 wavelet. This shows all

wavelets used do not give same performance for a given algorithm.

Figure 5.9 Histogram of Images using coif1 wavelet – X axis shows

Integer wavelet coefficient values and Y axis shows Number

of occurrences

Table 5.7 shows that cameraman image has a better embedding

capacity than other images in the experiment. It also shows it has a better

visual quality as far as Peak signal to noise ratio is concerned. Figure 5.10

shows the image quality tested for different images using integer wavelet

(a) Histogram of Cameraman image using coif1

wavelet - 14884 bits maximum embedded

(b) Histogram of boat image using coif1

wavelet -15625 bits maximum embedded

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transform for different payloads using cdf2.2 wavelet .The sailboat image

though has higher quality for the same payload compared to Lena image using

lower payload, the image quality quickly falls down as payload is increased.

This is because the wavelet histograms of Lena image have higher valued

peak points than sailboat image. To embed a given payload more shifting is to

be done on sailboat image compared to Lena image which degrades the

watermarked image quality.

Figure 5.11a through Figure 5.11f shows the image quality tested

on cameraman image for different payloads. It varies from 48.75 dB at 0.2

bits per pixel of the watermarked data embedded in the original image to

42.13 dB at 0.6 bits per pixel.

Image Quality Tested on different images using

cdf2.2 by varying Payload

36

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46

48

50

0 0.1 0.2 0.3 0.4 0.5 0.6

Payload (bpp)

Imag

e Q

uali

ty (

PS

NR

dB

) Lena

Cameraman

Woman dark hair

Sailboat

Figure 5.10 Image Quality vs Embedding Capacity of different

Images using cdf 2.2 wavelet

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Table 5.7 Performance of cdf2.2 wavelet using Image Quality Tested

on Different Grayscale Images for each payload

Lena CameramanWoman dark

hairSailboat

Sl.No Payload bpp

PSNR dB PSNR dB PSNR dB PSNR dB

1 0.1 46.3338 48.8519 47.5824 47.2015

2 0.15 45.9927 48.7678 47.5095 44.50572

3 0.2 43.6043 48.7591 47.5146 43.1822

4 0.25 42.1267 48.7421 47.4691 41.5522

5 0.3 40.1725 48.7014 47.4321 39.5865

6 0.35 39.3304 48.6990 45.7253 38.4327

7 0.4 37.8965 48.6552 45.0346 37.0477

8 0.45 insufficient 48.5825 43.8402 insufficient

9 0.5 insufficient 46.5234 42.2365 insufficient

10 0.55 insufficient 42.1261 insufficient insufficient

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(a) (b)

(c) (d)

(e) (f)

Figure 5.11 Watermarked Images of Cameraman (a) Original Image (b)

Payload(bpp)0.2, PSNR 48.75dB, (c)Payload(bpp)0.3, PSNR

48.70 dB, (d) Payload(bpp)0.4,PSNR 48.65 dB, (e) Payload

(bpp) 0.5, PSNR 46.52 dB, (f) Payload(bpp)0.6, PSNR

42.13dB

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5.5.1 Histograms of Original and Watermarked Image

Histograms of Original image get shifted in position after

embedding the watermark data. This is illustrated for cameraman image in

Figures 5.12a and 5.12b. These figures show the original wavelet histogram

and the histogram of the image after embedding the watermark.

Figure 5.12 Histogram of Cameraman Image Before and After

Embedding (a) Histogram of Cameraman Image before

embedding

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Figure 5.12 (b) Histogram of Watermarked Cameraman Image

Figure 5.12 shows histograms of Cameraman Image using cdf 2.2

wavelet. Figure 5.12(a) shows its histogram before embedding.

Figure 5.12(b), shows the watermarked cameraman image wavelet histogram.

This shows the shifted positions of the histogram points due to shifting and

embedding. Histogram of wavelet transformed cameraman image shows more

number of coefficient values at peak point compared to Lena image and

sailboat. This influences the embedding capacity. Cameraman image has

higher embedding capacity compared to Lena or sailboat image as illustrated

in Figures 5.12 and 5.13.

All images have different decompositions even if we use a

particular wavelet. The number of points in the peak of the wavelet histogram

influences the embedding capacity as we choose the peak for embedding the

watermark data. The following Figures 5.13a and 5.13b, illustrates the

histogram of Sailboat and Lena image after decomposing using cdf 2.2

wavelet. The peaks have lesser number of points compared to other images

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like cameraman and woman dark hair which has higher embedding capacity

due to higher peak points in the wavelet histogram.

Figure 5.13 Histogram of Images with lesser points in wavelet peaks

– X axis shows Integer wavelet coefficient values and Y axis

shows Number of occurrences (a) Histogram of Sailboat

Image after IWT (b) Histogram of Lena Image after IWT

5.6 CONCLUDING REMARKS

Reversible image watermarking using histogram shifting method

was done and tested using different images. Embedding capacity not only

varies from image to image, it also varies for various wavelets .The wavelet

histogram is used for embedding as it has a Laplacian like distribution and

embedding can be done on both sides of the histogram to embed more data.

More image quality is achieved for the same payload compared to other

reversible watermarking methods this is a blind watermarking method.

Original image and the embedded data are extracted exactly without any loss

because this method is completely reversible. Images with more number of

points on the wavelet histogram peak can embed more data. Cameraman

image performs better than all the other test images used for this algorithm.

Some wavelet decompositions and the wavelet histograms are not able to

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embed the desired capacity due to insufficient number of points in the wavelet

peak of the wavelet histogram.

The performance of the image watermarking algorithm using

histogram shifting is studied like the other algorithms implemented in this

work namely the reversible watermarking method using bitplane coding and

the non reversible neighbor correlation authentication method.

The performance of these algorithms will not be the same under the

influence of noise in the watermarked image. To see the behavior of all the

three algorithms implemented in such conditions, noise is introduced in the

watermarked image and the performance of the algorithms is studied and

discussed in the forthcoming chapter.

10 Chapter 5

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Transcript of 10 Chapter 5