visible video watermarking using orthogonal components

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 2 Issue: 9 2621 – 2626

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IJRITCC | September 2014, Available @ http://www.ijritcc.org

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Visible Video Watermarking Using Orthogonal Components

Lokendra Singh#1

, Ashwani garg*2

,Sandeep Srivastav#3

#1M.tech student- Sobhasaria Engineering College, Sikar

*2Associate Professor, CSE, Sobhasaria Engineering College, Sikar #3Lecturer, CSE, Sobhasaria Engineering College, Sikar

Abstract--Today, Digital data security cover such topics as authentication, copyright protection and access control for still images, video, audio

and multimedia products. Thus watermarking technique may be relevant in these areas of protection. Based on their domain embedding,

watermarking schemes can be classified as Transformed Domain or Spatial Domain. This thesis presents a process to mark digital pictures with

visible and undetectable hide information, called watermark. This process may be the basis of a complete Copyright Protection System(CPS).

Digital media, applications, copyright protection, and multimedia security become very important. Digital watermark is a technology used for

the copyright protection of digital media, digital applications. Watermark can be done by using Discrete Wavelet Transform (DWT) and

Principal Component Analysis (PCA). Experimental result is evidence for high imperceptibility where there is no noticeable difference between

the original frames and the watermark video frames. In this thesis we have a combination of the two conversions improves performance of the

watermark algorithm. We introduce the new concept of key-dependent-basis functions and discuss its applications to secure robust watermarking

for copyright protection.

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1 INTRODUCTION

The use of digital video has grown dramatically in recent

times. Digital video applications include video and audio

conferencing, video-on-demand, digital TV, digital cinema,

distance learning program, entertainment programs,

surveillance, and advertising. Many users experience digital

video when they watch a motion picture recorded on a

digital videodisc (DVD). Therefore, owners and creators of

digital products are concerned about illegal copying of their

goods or products. As a result, security issue and copyright

protection are becoming important issues in multi-media

applications.

Watermarking is a pattern of bits inserted into a

digital image (like as .jpg, .jpeg, .png etc.), video or audio

file that identifies the file's copyright information (author

name, rights, publication etc.).The name comes from the

faintly visible watermarks imprinted on stationery that

identify the manufacturer of the stationery. The aim of

digital watermarks is to provide copyright protection for

rational property that's in digital format.

2 LITERATURE SURVEY

2.1 Digital Watermarking

Digital watermarking requires many different elements from

many disciplines, including signal processing system,

telecommunications network, cryptography, psychophysics,

and law. We focus on the process of embedding and

retrieving watermarks in formatted text documents, images

file and video file [1]. We therefore emphasize the signal

processing and telecommunications aspects of watermarking

techniques because digital watermarking techniques is a new

topic, unless watermarks can be reliably introduced and

recovered, higher level issues such as protocols and security

are moot [2].

2.2 Classification

Watermarking techniques can be differentiated into the

following four categories according to the type of the

multimedia document to be watermarked:

Fig. 2.1 Classification of watermarking.

An effective watermark techniques should have several

properties and attributes-Robustness,

Imperceptibility,Security, Multiple watermarks.whose

importance will vary depending upon the application of

watermarking [3].Continuous efforts are being made to

machine an efficient watermarking schema [4] but

techniques proposed so far do not seem to be robust to all

possible attacks and multimedia data processing operations.

To transfer an image to its frequency representation, one can

use several reversible conversions like Discrete Cosine

Transform (DCT), Discrete Wavelet Transform (DWT), or

Discrete Fourier Transform (DFT) [5].

2.3 The Spatial Domain Techniques

The simplest example of spatial domain watermarking

techniques to insert data into digital signals in noise free

environments is least significant bit coding.

2.4 Transformed Domain Techniques

Generally Discrete Cosine Transform (DCT), Discrete

Fourier Transform (DFT) and Discrete Wavelet Transform


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(DWT) are used as the methods of data changes. In these

methods, a watermark that one wishes to embed distributive

in overall domain of an original data, and the watermark, is

hardly being destroyed once embedded.

2.5 Application of digital watermarking

Digital watermarking has been widely and successfully

applied in billions of media objects across a wide range of

applications. We will introduce some applications of digital

watermarking in both traditional and novel areas.

Copyright Protection -

Source Tracking

Broadcast

Fingerprinting

Copy protection

Broadcast monitoring

Data authentication

Medical safety

Data Hiding

3 PROPOSED APPROACH

3.1 Discrete wavelet transform

DWT is the discrete variant of the wavelet transform.

Wavelet transform represents valid alternative to the cosine

transform used in standard JPEG. The DWT of images is a

transform based on the tree structure with D levels that can

be implemented by using an appropriate collection of filters.

Most image watermarking schemes operate either in the

Discrete Cosine Transform (DCT) or the Discrete Wavelet

Transform (DWT) domain [10, 11]. A few watermarking

algorithms employ more exotic transforms such as the

Fourier-Mellin Transform and the fractal transform.

In numerical analysis and functional analysis, a discrete

wavelet transform (DWT) wavelet is tested rigorously, for

which a wavelet transform. As with other wavelet

transforms, Fourier transforms temporary solution but it is

an important advantage: the frequency and location

information (time, place) both capture.

3.2 DWT and Filter Banks

3.2.1 Multi-Resolution Analysis using Filter Banks

Filters are one of the most widely used signal processing

tasks. Walking can be realized by wavelets filters with

rescaling. One indication of the resolution measure of the

amount of detail information in a sign is determined by

the filtering operation, and scale up the sample is

determined by and down sampling (sub sampling)

operations.

The DWT is computed by successive low pass and high pass

filtering of the discrete time- domain signal as shown in

figure 3.1. This is called the Mallet algorithm or Mallet-tree

decomposition. Its significance in the manner it connects the

continuous-time mutire solution to discrete-time filter. In the

picture, the signal is denoted by the where n is an integer

sequence x [n].Low pass filter is denoted by Go while the

high pass filter is denoted by H0 At each level high-pass

filters detail information, d[n], is associated with low-pass

filter, while scaling function produces coarse approximate,

a[n]. high-pass filters detail information, d[n], is associated

with low-pass filter, while scaling function produces coarse

approximate, a[n].

Fig. 3.1 Three level wavelet decomposition tree.

At each decomposition level, the half band filters produce

signals spanning only half the frequency bands. This

doubles the frequency resolution the uncertainty in

frequency is reduced by half. According to Nyquist's rule if

original signal has a highest frequency of ω, which require a

sampling frequency of 2ω radians, then it now has highest

frequency of ω/2 radians. It now be sampled at a frequency

of ω radians thus discarding half the samples with no loss of

information. This decimation by 2 halves time resolution as

the entire signal is now represented by only half the number

of examples. Therefore, while the half-band low-pass filter

removes half of the frequencies and thus halves resolution, 2

double the scale of destruction [14].

With this approach, the time resolution becomes arbitrarily

good at high frequencies, the frequency resolution becomes

arbitrarily while good at low frequencies. DWT of the

original signal is then obtained by concatenating all the

coefficients, a [n] and d [n], starting from the last level of

decomposition [15].

Fig. 3.2 Three-level wavelet reconstruction tree

Figure 3.2 shows the reconstruction of the original signal

from the wavelet coefficients. In fact, the reverse

reconstruction process of decomposition. Approximation

and detail coefficients at every level are two upsampled,

passed through low pass and high pass synthesis filters and

then added [16].

3.2.2 Conditions for Perfect Reconstruction

In most Wavelet Transform application, it is necessary that

the original signal be synthesized from the wavelet


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j

coefficients. Let G0 (z) and G1 (z) be the low pass analysis

and synthesis filters, respectively and H0 (z) and H1 (z) high

pass analysis and synthesis filters respectively. The filter has

to satisfy the following two conditions as given in:

G0 (-z)G1 (z) + H 0 (-z).H1 (z) = 0 (3.1)

G0 (z)G1 (z) H 0 (z).H1 (z) 2z-d (3.2)1

The first condition implies that the reconstruction is

aliasing-free and the second condition implies that the

amplitude distortion has one of the dimensions. The

accuracy of Wavelet Transform be determined after

reconstruction by calculation the Signal to Noise Ratio

(SNR) signal [17].

After the original image has been DWT transformed, it is

decomposed into 4 districts frequency which is one low-

frequency district (LL) and three high-frequency district

(LH, HL, HH). If information of low-frequency district is

DWT transformed frequency sub-district level information

will be obtained. After a two-dimensional image three times

DWT decomposed can be shown as Fig.3.5. Where, L

represents low-pass filter, H show high-pass filter. An image

can be decomposed of frequency HL1, LH1, and HH1

districts. Low frequency district Information can also be

decomposed into sub-level frequency district LL2, HL2,

LH2 and HH2 information. By doing this the original image

can be decomposed for n level wavelet transformation.

Fig. 3.5 Sketch Map of Image DWT Decomposed

According to the human character eyes are sensitive to

changes district of smooth image, but not sensitive to the

tiny torrent, profiles and changes streak [21]

3.3 Principal component analysis

Principal component analysis (PCA) is a mathematical

procedure that uses an orthogonal transformation to convert

a set of observations of possibly correlated variables into a

set of values of uncorrelated variables called the major

components. Reduce the number of principal components

than or equal to the number of the original variables. The

first major component of change (the variability in the data

as much the largest as possible, which accounts) possible

variance is defined this way, and instead hit the highest

possible variance under the constraint that each (i.e.

uncorrelated) components are orthogonal to preceding

components.

3.3.1 Definition of principal component expansions

In this section we summarize several known properties of

principal expansion component. X denote a random

function, or rather two a the stochastic process that I = (0,1)

is defined in the interval and satisfying IE ( X 2

) < . Put

η = E( X ), a conventional function. The principal

component expansion of may be constructed via the

covariance function.

K (u, v) E[{X (u) (u)}{X (v (v)}] (3.4)

Which we assume to be square integral and interpret as the

kernel of mapping, or operator, on the space L2( I ) of

square integral functions from I to real line. We denote the

combination will reduce nominal load the operator by K,

also; takes L2(I ) to , where

( )(u) IK (u, v) (v)dv

(3.5) Then we may write,

K (u, v) jj (u)j (v)

Where 1 2 ...... is an enumeration of the K

eigenvalues, eigenfunctions and generic ortho are 1 2. . . .

. . .

The Karhunen-Loeve expansion of is give

(3.6)

Where the random variables 1, 2......are given by j I(

X )j (u) .

It follows that they uncorrelated and have means zero, and

that j E( 2

) and

(3.7)

4 METHODOLOGY

4.1 Algorithm for watermarking using DWT and PCA

All valid generators are required to be non-invertible

functions of x and ksec ret . In other words, a valid

generator G cannot have an efficiently computable inversion


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function G-1: W → K such that G(x, G-1(w)) = w for a

given watermark w.

Algorithm 1:

a) Embedding Procedure

Step 1: Convert the n × n binary watermark logo into a

vector W = {w1, w2 , ……, wn × n } of

“0s” and “1s”.

Step 2: Divide the video (2N × 2N) into distinct frames.

Step 3: Convert every frame from RGB to YUV color

format.

Step 4: Apply 1-level DWT to the luminance (Y component)

of each video frame to obtain four sub-bands LL, LH, HL

and HH of sizes N x N.

Step 5: Divide the LL sub-bands into k non-overlapping

sub-blocks each of dimension n × n the same size as

watermark logo).

Step 6: The watermark bits were embedded with strength α

into each sub-block by first obtaining the principal

component scores by Algorithm 2. The embedding is carried

out equation 1.

score i + score i = αW (4.1)

where i score represents the principal component matrix of

the ith sub-block.

Step 7: To apply inverse PCA on the modified PCA

components of the sub-blocks of the LL sub- bands to obtain

the modified wavelet coefficients.

Step 8: Apply inverse DWT to obtain the watermarked

luminance component of frame. Then convert video frame

back to its RGB components.

b) Extraction Procedure

Step 1: Divide the watermarked (and possibly attacked)

video into distinct frames and convert it from RGB to YUV

format.

Step 2: Choose the luminance (Y) component of a frame and

apply the DWT to decompose the

Y components into the four sub-bands LL, HL , LH , and

HH of size N×N.

Step 3: Divide the LL sub-band into n × n non overlapping

sub-blocks.

Step 4: Apply PCA to each block in the selected sub- band

LL by using Algorithm 2.

Step 5: From the sub-band LL, the watermark bits are

extracted from the principal components of each sub-block

as in equation 2.

Wi = (scorei – scorei) / α (4.2)

where 'i W is the watermark extracted from the ith subblock.

Algorithm 2:

LL sub-band coefficients are changed into a new coordinate

set by calculating the principal components of each sub-

block (size n x n).

Step 1: Every sub-block is converted into a row vector Di

with n2 elements (i=1,2…k ).

Step 2: Calculate the mean μi and standard deviation σi of

the elements of vector Di .

Step 3: Compute Zi according to the following equation

Z i =(Di - µi) / σi (4.3)

Here i Z represents a centered, scaled version of Di , of the

same size as that of Di .

Step 4: Principal Component Analysis on i Z (size 1 x n2 )

to obtain the principal component coefficient matrix coeffi

(size n2 × n2).

Step 5: Calculate vector i score as

Score i = Z i × coeff i (4.4)

where i score represents the principal component scores of

the ith sub-block.

4.2 Verification of the result

The MSE (Means Square Error) and NC (Normalized

Coefficients) values are calculated for the watermarking

procedure. And the criterion of good watermarking

technique is, lower should be the MSE value and higher

should be the NC value. MSE represent the similarity index

of original image in comparison to watermarked image.

Better is resemblance better is watermarking scheme

(A) PSNR : The Peak-Signal-To-Noise Ratio (PSNR) is

used to evaluate deviation of the attack of the watermarked

and the original frame video frames and is defined as:

PSNR := 10 Log10 (2552 / MSE ) , measured in

dB(decibels) units. Where, MSE (mean squared error)

between original and distorted frames (size m x n) is defined

as: m n

MSE : 1/(m * n)jI (i, j) I ' (i, j)

I and I’ are original and watermarked respectively.

(B) The Normalized Coefficient (NC) gives a measure of the

robustness of watermarking.

NC can be formulated as:

W and W’ represent the original and extracted watermark

respectively.

5 RESULT ANALYSIS

The result shown by the below figures our watermarking

technique can be used both in semi visible and invisible

watermarking technique. It can be proved that for the

dynamic range of alpha that targeted semi visible and

invisible watermarking can be achieve.

The robust visible watermarking has been a topic of

considerable interest due to their potential use for

copyright protection As pointed out in the ability to put

robust watermarks does not necessarily solve the

ownership problem. Still lots of work need are to be done

in order to make the robust invisible watermark legally

useful.


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Experiments with Basic Images:

Experiment no:1

(a) (b)

Fig 5.1 (a)Main Image 1 (b) watermark 1

Final result after Visible Watermarking

Fig. 5.2 after watermarking

Graph representation of the performance

Fig 5.3 Graphical view of watermarking performance

Experiment no: 2

(a) (b)

Fig 5.4 (a) Main Image 1 (b) watermark 2

Final result after Visible Watermarking

Fig. 5.5 After watermarking

Graph representation of the performance

Fig 5.6 Graphical view of watermarking performance

Final Result Analysis for 2 Experiments:-

Experiment I Experiment II

PSNR NC PSNR NC

103.1111 0.862043 103.1837 0.870634

103.8549 0.853072 103.9979 0.872016

104.4118 0.858228 104.6117 0.862989

104.7406 0.86739 104.9977 0.8756

104.8929 0.869099 105.1966 0.85865

104.8303 0.846448 105.1901 0.858212

104.4155 0.892957 104.8139 0.940657

103.9879 0.865084 104.4167 0.862318

103.5091 0.884885 104.0099 0.907749

102.8776 0.879634 103.583 0.922249

Peak-Signal-To-Noise Ratio (PSNR)

Normalized Coefficient (NC)

6 CONCLUSION AND FUTURE SCOPE

As a future scope the concept of Cryptography and Digital

Watermarking can be combined to implement more secure

Digital Watermarking system. We can use the watermarking

technique in the frequency domain of various applications

watermark, image watermark as the. We can also implement

in other spatial domain techniques and cryptography

algorithms for most advanced encryption technique to

encrypt the messages.

Watermarks may be viewed with software and can reveal

either a unique identification code that can be traced to the

copyright owner or specific information about the copyright

owner. Companies are also offering on-line tracking

services so that the copyright owner can see how the owner's

materials are used via the Web. These processes will

continue to help fight against electronic copyright

infringement.

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visible video watermarking using orthogonal components

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