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A Robust Watermarking method based on. Compressed Sensing and Arnold scrambling. Veena V K, Jyothish Lal G, Vishnu Prabhu S, Sachin Kumar S, Soman ...
A Robust Watermarking method based on Compressed Sensing and Arnold scrambling Veena V K, Jyo this h Lal G, Vishnu Prabhu S, Sachin Kumar S, Soman K P

Centre For Excellence in C o mputational Engineering and Net w orking Amrita Vishwa V idyapee tham Coimbatore-641112, India

veena-vk89(Q:gmail.com

Abstract--Watermarking is a technique for information hid­

leads to waste of data calculation. Besides compression, the

ing, which is used to identify the authentication and copyright

secrecy of the data is also having prim ary importance. Hence

protection. In this paper, a new method of watermarking scheme is proposed, which uses both Compressed Sensing and Arnold

scrambling m e thod for efficient data compression and encryption.

encrypt ion of the compress ed data is performe d follow ing the compression. This step is performed cither by conventional

Comprcssh'c sensing tcchniquc aims at thc rcconstruction of

cryptograph i c a lgo rithms or s ome custom

sparse signal using a small number of linear measurements.

pression and encryption

Compressed measurement.. are then encrypted using Arnold

transform. The proposed encryption scheme is computationally more secure against investigated attacks on digital multimedia signals. Keywords-Watermarking, Compressh'e sensing, Arnold scram­ bling Method, Encryption, Decryption.

1.

com­

me thod is proposed to improve the secrecy of w at ermarked image. The se curity is don e in two stages namel y Co mpres s ive Sensing and Arnold scrambling. Compressive s ampling uni l1es

the

sampling, compressio n and encryption of the data to ,

be sent. This is achieved by collecting linear measurements

y=Ax of a sparse signal

is deve lopi ng

design j oint

In this paper, a new

x

where 'A: is the linear transform

carrying certain regu lar iti es . The lin e ar measurements 'y' are

INTRODUCTION

No waday s te chno log y

schemes [I].

fu ncti on of sensing matrix 'A:

at a rate faster than

pseudo-random

entries is

[2]. The matrix 'A: having

ge nerate d us ing a secret key, w hi ch

multimedia signals has increased more in the internet The security as­

is known only to the sender and receiver. Thus Compressed

pect plays a major role in many applications such as video

representation of original signal. During the second level of

speed of light and the amount of redundant digital .

surveillance, confidential data tr ansmis sion in military and

medical fie lds , as the dig ita l

sig nals

are transmitted throug h

insecure transmission channel or network. So to discourage the

unauthorized c opying and distribution of the digital

Sensing measurements can be considered as an encrypted securi ty, the compre ssed and encry pt ed data obtained from the

first

level is subj ect ed t o Arno l d

scrambling This method of .

encryption scrambles the data in a certain I1xed number of

data, one

ite ration , which is known only to the authe nticated receiver.

has to protect intelligence information and intellectual property

Thus the receiver is able to decrypt the original data with

of the digital media. Watermarking is a unique technique for protection of intellectual property

ri ghts. The owners hip

information is hidden in the multimedia object, which can be

further

extracted or decoded for authentication purposes. It can

be in the form of text, image or any logo and the application

the aid of these secret keys . Expe rim ental results shows that

the combined security

of Co mpre ssive Sen sing a nd Arn old

scrambling proposed in this paper is computationally more

secure

against inv estigated attacks on sensitive data.

This paper is organized as follows. Section 2 gives a brief

decides which one to be used as watermark.

introduction a b out watermarking. Section 3 pr ovides a brief

is b and limited, it is desirable to compress the digital data prior to tnmsrnission for efficient util ization of memory/storage resources and co mm unic a tio n bandwidth. The conventional e ncoding process starts with

Compressive Se nsi ng scheme. Section 4 explains Arnold scrambling method. Section 5 describes the pr o po sed enc rypt ion and decryption scheme. S ect ion 6 gives the experimental results and final ly section 7 concludes the

sampling of the signal and then the sampling values are

paper.

As the transmission channel

quantized and coded for transmission or storage. The decoding

process is the reverse of encoding . But conventional appro ach

intro du cti on abo ut

II. WATERMARK EMBEDDING SCHEME

s trict l y fo llows N yquist Sa mpl ing theorem which states that

A watennar k is a s eco n dary i m age , label or m es sage that

the sampling frequency should be greater than or equal to

is embedded to the original image. The objective of the

bandwidth, making more complication in

watermarking scheme is to embed some piece of distinguished

the hardware design. In addition to that, lots of little coeffi­

information within an image. So the modified output is a

cients are discarded during conventional compr es si o n , which

wat e rmarked image. The different algo r ithms used are discrete

tw o tim es me ssage

978-1 -4673-2322-2/1 2/$31.00 ©2012 IEEE 105

TABLE I Two DIMENSIONAL ARNOLD TRANSFORM PERIOD WITH DIFFERENT DEGREE N

Fourier, Cosine, wavelet transformation and fractal approaches for digital images. In general, the watermark is integrated into the image compo­ nents by a factor that allows amplification of the watermarking values in order to obtain the best results.The principle of correlation is used in detecting the watermark in the image.

5 6 7 10 12 8 N 32 40 48 50 56 60 Period 24 30 12 150 24 60

N 2 Period 3

3 4

4 3

9 10 11 12 30 5 64 100 12 125 48 150 60 250 8 6

12 12

16 12 128 256 96 192

24 12

25 50 480 512 120 384

It automatically detects the presence of watermark in the image based on a specific correlation level by comparing with original image. This method usually enables the in­ tegration of one bit watermark information. On integrating more information, the watermarking detector generates a bit sequence corresponding to a specified correlation level. In this paper, digital watermarking is done in the spatial domain by embedding the text information in the bth bit plane of the

using linear programming techniques whose computational complexity is of the order of O(N). To recover back x from y, it is required to estimate sparse solution to y=fW, where

min

III. COMPRESSIVE SENSING SAMPLING reconstructing the signals and images from fewer samples.

¢\II. Once

0 is known with the

IIfl11 subject toy

=

no

(2)

Thus the problem get changed into linear programming which is quite straight forward.

Compressive Sensing achieves this by two principles: sparsity

IV. ARNOLD SCRAMBLING METHOD

and incoherence. A.

=

be expressed as

image.

Compressive Sensing theory is the process of acquiring and

n

knowledge of \II x can be recovered. The sparse solution can

Arnold transformation is posed in the research of Arnold

Sparsity of the signal and incoherence

ergodic theory, which is also called cat face transformation.

Compressive sampling normally relies on the sparse nature

It is a chaotic transformation proposed by V.I Arnold. It

of the signal. Most of the natural signals are sparse in nature

is used in the digital image scrambling process because of

and therefore can be represented as in a concise manner

its iterative periodicity, good decentralization etc. Therefore

when they are expressed in the proper basis \II . Incoherence

it finds application in the field of image encryption, digital

gives the idea that object or data with sparse representation

watermarking etc. The transform process realign the pixel

in \II must be spread out in the domain in which they are

matrix by means of clipping and splicing. Two dimensional

acquired. More precisely, the sensing/sampling waveform have

Arnold transformation is defined as follows:

an extremely dense representation in \II . Let x be a real valued signal of length N which can be E

represented as x=\IIO where \II

RNxN is an orthonor­

mal basis matrix(or dictionary) that can provide a K sparse

1 representation of x E RNx and 0 E RNx 1 can be well approximate using only K <