CHAPTER 2 REVIEW OF LITERATUREshodhganga.inflibnet.ac.in/bitstream/10603/9089/6/06_chapter 2.pdf ·...

Information Hiding in Image and Audio Files 2007-2010

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CHAPTER 2

REVIEW OF LITERATURE

Information hiding techniques can be classified into two categories,

namely irreversible information hiding schemes [26, 29, 30] and

reversible (lossless) information hiding schemes [32]. In irreversible

information hiding schemes, only secret data are extracted and no

restoration of cover objects is made. In contrast, in reversible

information hiding schemes, secret data are extracted and cover

objects can be completely restored. Reversible hiding schemes are

suitably used for some applications such as the healthcare industry

and online content distribution systems. In this thesis information

hiding is done using irreversible information hiding schemes.

2.1. Domain of Information Hiding

The existing schemes of information hiding in images and audio can

roughly be classified into the following three categories:

Spatial domain / Time domain

Transform domain

Compressed domain

2.1.1 Image files

(I)Spatial domain Information hiding

Data hiding in spatial domain [33-35] type directly adjust image

pixels in the spatial domain for data embedding. This technique is

simple to implement, offering a relatively high hiding capacity. The

quality of the Stego image can be easily controlled. Therefore, data

hiding of this type has become a well known method for image

steganography.


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(a)Least Significant Bit Techniques

A large number of commercial steganographic programs use the

Least Significant Bit embedding (LSB) as the method of choice for

message hiding in 24-bit, and 8-bit images. The LSB insertion

method is a common, simple approach to embedding information in

a graphical image file. Unfortunately, it is extremely vulnerable to

attacks, such as any image manipulation.

The terminology LSB replacement/ LSB matching was discussed by

T.Sharp [14]. LSB substitution [27] algorithm is the simplest

scheme to hide message in a host image. It replaces the LSB of

each pixel with the encrypted message bit stream. Authenticated

receivers can extract the message by deciphering the LSB of every

pixel of the host image with a pre-shared key. Since only the LSB of

pixels is altered, it is visually imperceptible by human eye. The

capacity of the algorithm is 1 bit per pixel (bpp). Although this

algorithm is visually imperceptible, it can be statistically analyzed by

other entity without processing the pre-shared key.

The LSB insertion method is the most common and easiest method

for embedding messages in an image with high capacity, while it is

detectable by statistical analysis such as Regular and Singular (RS)

and Chi-Square analyses. The method proposed by Hong-juan

zhang et al., [37] is a novel LSB image steganography algorithm

that can effectively resist image Steganalysis[28,96,97,98,99]

based on statistical analysis. Every two sample‟s of LSB bits are

combined using addition modulo 2 (or m) to form the value which is

compared to the part of the secret message. If these two values are

equal, no change is made. Otherwise, the difference of these two

values is added to the second sample. Thus, the part of the secret

message can be embedded effectively. RS and Chi-Square analyses

are performed on stego-medium created using the steganography


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technique. The experiment showed that the scheme has the same

insertion capacity and Signal to Noise Ratio (SNR) as the classic LSB

steganography and the proposed method can effectively resist

steganalysis based on RS and Chi-Square analyses.

A similar idea called LSB matching has been proposed by Ker [38]

to improve LSB substitution. Yet it is also vulnerable to other

designated detection algorithms. This is owing to the histogram of

the host image is changed. A revised version of LSB matching is

proposed by Mielikainen [39] in 2006. This method greatly improves

the above two methods by lowering the expected number of

modifications per pixel, from 0.5 to 0.375. Therefore, the histogram

affected by the scheme is less significant. Only a few detection

methods for LSB matching have been proposed.

In another data hiding technique by variable depth LSB

substitutions proposed by Smo-Huliiv et al., [40], pixels of cover-

image are grouped according to themselves by luminance values.

Then the occurrence of pixel-values in each group is counted and

sorted in monotone increasing. Finally a bit plane-wise data hiding

method is used to hide data in cover image. And the worst-square-

error between the stego-image and the cover-image is formulated;

Theory analysis indicates that image quality of the stego-image

hidden by this method can improve from 0 decibels (db) to 4db

against simple LSB substitution method [40].

A novel approach of image embedding was introduced by Rehab et

al., [41]. The method consists of three main steps. First, the edge

of the image is detected using Sobel mask filters. Second, the LSB

of each pixel is used. Finally, a gray level connectivity is applied

using a fuzzy approach and the American Standard Code for


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Information Interchange (ASCII) code is used for information

hiding.

A novel data hiding technique is proposed by Dey et al., [42], as an

improvement over the Fibonacci LSB data-hiding technique. The

novel technique is based on decomposition of a number (pixel-

value) in sum of prime numbers. The particular representation

generates a different set of (virtual) bit-planes altogether, suitable

for embedding purposes. They not only allow one to embed secret

message in higher bit planes but also do it without much distortion,

with a much better stego-image quality, and in a reliable and

secured manner, guaranteeing efficient retrieval of secret message.

A comparative performance study between the classical LSB

method, the Fibonacci LSB data-hiding technique and these

proposed schemes has been done. Analysis indicates that image

quality of the stego-image hidden by the technique using Fibonacci

decomposition improves against that using simple LSB substitution

method, while the same using the prime decomposition method

improves drastically against that using Fibonacci decomposition

technique. Experimental results have shown that, the stego-image

is visually indistinguishable from the original cover-image.

Daniela et al., [43] used the LSB technique in YUV color space to

embed the secret message. The secret message was hidden in the V

plane and the image was converted back to RGB to obtain the stego

image.

A new method using the RBTC and LSB substitution to hide data is

proposed by Ching-Yu Yang [44]. Based on the RBTC, a simple LSB

substitution is employed for embedding secret data to the resulting

compressed images. Experiments show that the PSNR and hiding


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rate for the proposed method are good while the perceived quality

is not bad. Since the perceptual quality of the mixed images are

close to that of compressed images generated by the RBTC without

hiding capability, the proposed method possesses a merit of being

attracted hardly by the grabbers. In other words, the grabbers do

not easily notice the existence of the embedded message.

An effective color image steganography method based on the

module substitutions is proposed by Ching-Yu Yang [45]. In

accordance with the base-value of the blocks, a variety of secret

bits is embedded to a RGB trichromatic. system by three types of

module substitutions More specifically, to alleviate further color

distortion and obtain a larger hidden capacity, the R-, G-. and B-

components are encoded by Mod u, Mod u-v, and Mod u-v-w

substitution, respectively. Experiments show that both PSNR and

hiding rate generated by the proposed method are better than

those generated by the reported schemes. In addition, the resulting

perceptual quality is good.

Block Truncation Coding (BTC) is an efficient compression technique

while offering good image quality. Nonetheless, the blocking effect

inherent in BTC causes severe perceptual artifact in high

compression ratio applications. In this proposed method by Jing-

Ming Guo [46], an Error-Diffused Block Truncation Coding (EDBTC)

is proposed to solve this problem. According to the EDBTC, the error

caused by the difference between the original grayscale pixel value

and the correspondingly high or low mean substitute is diffused to

the predefined neighborhood, and hence the average grayscale will

be maintained invariably. In addition, since the compressed data

are widely distributed in the internet transmission, the extra

message conveying in a secret way also highly raises attention.

Recently, in this method, the Complementary Steganography in


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Error-Diffused Block Truncation Coding (CSEDBTC), which

cooperates with error diffusion to achieve the objective of secret

communication in BTC images, is proposed. As documented in the

experimental results, a low complexity with good image quality is

obtained.

A novel data-hiding methodology proposed by Chun-Hsiang Huang

et al., [47], denoted as digital invisible ink (DII), is proposed to

implement secure steganography systems. Like the real-world

invisible ink, secret messages will be correctly revealed only after

the marked works undergo certain pre-negotiated manipulations,

such as lossy compression and processing. Different from

conventional data-hiding schemes where content processing or

compression operations are undesirable, distortions caused by pre-

negotiated manipulations in DII-based schemes are indispensable

steps for revealing genuine secrets.

The scheme proposed in [47] is carried out based on two important

data-hiding schemes: spread-spectrum [95] watermarking and

frequency-domain quantization watermarking. In some application

scenarios, the DII-based steganography system can provide

plausible deniability and enhance the secrecy by taking cover with

other messages. It is stated that DII-based schemes are indeed

superior to existing plausibly deniable steganography approaches in

many aspects. Moreover, potential security holes caused by

deniable steganography systems are discussed.

In the LSB technique the retrieval of the secret message can be

done by retrieving the LSB of the pixel value. From the security

aspect this technique is very fragile. To overcome this drawback, a

new robust technique is proposed by Kekre et al.,[48], in which

three different variations of LSB technique are proposed. These


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proposed techniques do not directly replace the LSB but LSB is

modified by taking into consideration the magnitude of the Cover

image.

(b)Increasing Capacity Techniques

In the LSB technique, only the LSB of every pixel value is utilized

for secret message embedding, this results in a constraint to the

embedding capacity in the cover image. In this section, the

techniques proposing an increase in the embedding capacity are

reviewed.

A new and efficient steganographic method for embedding secret

messages into a gray-valued cover image was proposed by Wu et

al., [36]. In the process of embedding a secret message, a cover

image is partitioned into non-overlapping blocks of two consecutive

pixels. A difference value is calculated from the values of the two

pixels in each block. All possible difference values are classified into

a number of ranges. The selection of the range intervals is based on

the characteristics of human vision‟s sensitivity to gray value

variations from smoothness to contrast. The difference value then is

replaced by a new value to embed the value of a sub-stream of the

secret message. The number of bits which can be embedded in a

pixel pair is decided by the width of the range that the difference

value belongs to. The method is designed in such a way that the

modification is never out of the range interval. This method

provides an easy way to produce a more imperceptible result than

those yielded by simple LSB replacement methods. The embedded

secret message can be extracted from the resulting stego-image

without referencing the original cover image.

In order to improve the capacity of the hidden secret data and to

provide an imperceptible stego-image quality, a novel


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steganographic method based on LSB replacement and Pixel Value

Differencing (PVD) method was presented by Wu et al., [36]. First,

a difference value from two consecutive pixels by utilizing the PVD

method is obtained. A small difference value can be located on a

smooth area and the large one is located on an edged area. In the

smooth areas, the secret data is hidden into the cover image by LSB

method while using the PVD method in the edged areas. Because

the range width is variable the area in which the secret data is

concealed by LSB or PVD method is hard to guess. The security

level is the same as that of a single LSB using the proposed PVD

method. From the experimental results, compared with the PVD

method being used alone, the proposed method can hide much

larger information and maintains a good visual quality of stego-

image.

A high quality steganographic technique is proposed by Wang et al.,

[50] with PVD and modulus function capable of producing a stego

image that is imperceptible from the original image by the human

eye. In addition, the method avoids the falling-off-boundary

problem by using PVD and the modulus function. First, a difference

value from two consecutive pixels is calculated using the PVD

technique. The hiding capacity of the two consecutive pixels

depends on the difference value. In other words, the smoother the

area is, the less secret data can be hidden; on the contrary, the

more edges an area has, the more secret data can be embedded.

This way, the stego-image quality degradation is more

imperceptible to the human eye. Second, the remainder of the two

consecutive pixels can be computed by using the modulus

operation, and then secret data can be embedded into the two

pixels by modifying their remainder. In this scheme, there is an

optimal approach to alter the remainder so as to greatly reduce the

image distortion caused by the hiding of the secret data. The values


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of the two consecutive pixels are scarcely changed after the

embedding of the secret message by the proposed optimal

alteration algorithm. Experimental results have demonstrated that

the proposed scheme is secure against the RS detection attack.

A new adaptive LSB Steganography method using PVD that provides

a larger embedding capacity and imperceptible stego images was

proposed by Yang et al., [51]. The method exploits the difference

value of two consecutive pixels to estimate how many secret bits

will be embedded into the two pixels. Pixels located in the edge

areas are embedded by a k-bit LSB substitution method with a

larger value of k than that of the pixels located in smooth areas.

The range of difference values is adaptively divided into lower level,

middle level, and higher level. For any pair of consecutive pixels,

both pixels are embedded by the k-bit LSB substitution method.

However, the value k is adaptive and is decided by the level which

the difference value belongs to. In order to remain at the same

level where the difference value of two consecutive pixels belongs,

before and after embedding, a delicate readjusting phase is used.

When compared to the past study of Wu et al.'s PVD and LSB

replacement method, experimental results show that proposed

approach provides both larger embedding capacity and higher

image quality.

The main requirements for a steganographic scheme are visual

imperceptibility and statistical undetectability. Steganographic

techniques which exhibit these two qualities have lesser embedding

capacity. So to achieve statistical undetectability with higher

embedding efficiency, matrix embedding proposed by Arjun et al.,

[52] is preferred. The Embedding efficiency is defined as the ratio of

change density to embedding rate. The change density using matrix

embedding is defined as the number of modifications that are


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needed per code sequence of length n, to embed a k bit message.

And embedding rate is defined as ratio of the number of message

bits k, which can be embedded in a code sequence n, with a single

modification. In [100] Cover is chosen such that it is correlated with

the covert messages thus reducing the biits needed to encode the

hidden message

A method of implementing image steganography in a color image

for applications such as covert communication via the Internet and

authentication of an employee carrying a picture identification card

is described by Kaliappan Gopalan [53]. By converting the color

image to a one-dimensional signal in red, green, or blue, audibly

masked frequencies in the one Dimensional (1-D) signal are

determined for each segment or block. Embedding of secure or

confidential data is carried out by modifying the spectral power at a

pair of commonly occurring masked frequencies. Compressing the

data-embedded image to standard Joint Photographic Experts

Group (JPEG) coding enables its transmission via the Internet.

Experimental results show that the technique is simple to

implement and causes barely noticeable distortion in the stego

image. Using an oblivious technique and a key consisting of the

frequencies where the spectrum is modified, successful data

retrieval even at low level noise levels and at low-loss compression

has been achieved. Higher payload of hidden data can be obtained

at a cost of perceptibility of embedding. Lossy JPEG compression,

however, leads to low payloads.

(II) Transform domain Information hiding:

In this method [54, 55] images are first transformed into transform

domain, and then data is embedded by modifying the transformed

coefficient

The spatial domain techniques embed the secret data within the

pixels of the cover image. The LSB (Least Significant Bit)


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substitution is an example of spatial domain techniques. The other

type of hiding method is the transform domain techniques which

embed the secret data within the cover image that has been

transformed using some transforms such as DCT (Discrete Cosine

Transform), DWT (Discrete Wavelet Transform), FFT (Fast Fourier

Transform) etc. The DCT or DWT coefficients of the transformed

cover image will be quantized and then modified according to the

secret data to be embedded.

There are many transforms that can be used in data hiding. The

most commonly used is Discrete Cosine Transform ( DCT) which is

used in the common image compression format JPEG and MPEG

[56], The discrete wavelet transform (DWT) [94] now a days is

used in new JPEG2000.

(a)An Adaptive Steganography technique based on integer

wavelet transforms

An Adaptive steganography technique based on integer wavelet

transform is presented in [57]. This technique tries to optimize high

hiding capacity and imperceptibility by proposing a novel technique

for hiding data in digital images. This is done by combining the use

of adaptive hiding capacity function that hides secret data in the

integer wavelet coefficients of the cover image with the OPA

(Optimum pixel Adjustment) algorithm. The coefficients are selected

according to the pseudo random generator to increase the security

of the hidden data. The OPA algorithm is applied after embedding

the secret data to minimize the embedding error.

(b)Secure Blind Image Steganography Technique using

Discrete Fourier Transformation

Another Image Steganography technique has been proposed using

DFT in [58].The technique embeds the hidden information in the


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DFT domain after permuting the image pixels in the spatial domain

using a key. The permutation process introduces randomness into

the cover image and results in a significant increase in the number

of transform coefficients that can be used to transmit the hidden

information. The information is embedded using quantization

technique.

(c) High Capacity Steganographic Method Based Upon JPEG

JPEG technique divides the input image into non-overlapping blocks

of 8x8 pixels and uses the DCT. The method discussed in [56]

divides the cover image into non-overlapping blocks of 16x16

pixels. For each quantized DCT block, the least two significant bits

of each middle frequency coefficients are modified to embed two

secret bits. The hiding technique proposed in [56] achieves better

hiding capacity than Jpeg-Jsteg methods which are based on the

conventional blocks of 8x8 pixels.

(d)An Effective Image Steganographic Scheme Based on

Wavelet Transformation and Pattern - Based Modification

This scheme [160] also hides a secret message in a digital image.

The scheme is called Pattern Based Image Steganography (PBIS).

First PBIS apply Discrete Wavelet Transform on the cover image;

separates the transformation result into non-overlapping blocks.

PBIS uses a secret key and a pseudo random number generator to

select some blocks. The scheme analyzes the pattern types of the

coefficients of the selected blocks and change the pattern types of

these selected blocks according to the secret message Finally the

DWT is applied to transform the wavelet coefficients to their spatial

domain and obtain the stego. Advantage of this scheme is that it

can survive under JPEG lossy compression.


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(e) High Capacity Image Steganography using Wavelet-

Based Fusion

The main idea of the proposed algorithm presented in [161] is the

wavelet based fusion. It involves merging of the wavelet

decomposition of the normalized versions of both the cover image

and the secret image into a single fused result. In a normalized

image the pixel components take on values that span a range

between 0.0 and 1.0 instead of integer ranges of [0-255]. Hence

the corresponding wavelet coefficients also range between 0.0 to

1.0. Before the embedding process, the system does a

preprocessing step on the cover image. This step involves shrinking

the range of the pixel color components in order to avoid any range

violation due to the embedding process. This ensures that the

embedding message will be recovered correctly. The extraction

process involves subtracting the original cover image from the stego

image in the wavelet domain to get the coefficients of the secret

message. Then the embedded message is retrieved by applying

inverse discrete wavelet transform (IDWT).

(III) Compressed domain Information hiding: Information

hiding in compressed domain [32, 59] is obtained by modifying the

coefficients of the compressed code of a cover image. Since most

images transmitted over Internet are in compressed format,

embedding secret data into the compressed domain would provoke

little suspicion.

(a)Vector Quantization for Information Hiding:

One of the most commonly studied image compression techniques

is Vector Quantization (VQ) [117-133] [60], which is an attractive

choice because of its simplicity and cost-effective implementation.

Indeed, a variety of VQ techniques have been successfully applied

in real applications such as speech and image coding [61, 62].


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Vector quantization has been very popular in variety of research

fields such as video-based event detection [133], speech data

compression [132], image segmentation [134-45], face recognition

[146, 147, 148], iris recognition [149, 150, 151], CBIR [152-155],

Colorization [156, 157] data hiding [158,159] etc. Vector

Quantization not only has faster encode/decode time and a simpler

framework than JPEG/JPEG2000 but it also requires limited

information during decoding, and those advantages cost VQ a little

low compression ratio and visual quality. VQ works best in

applications in which the decoder has only limited information and a

fast execution time is required [63].

The key point to the design of a perfect VQ scheme is to generate a

perfect codebook from the training images. The LBG algorithm,

proposed by Linde, Buzo and Gray in 1980 [64], gives a good

answer and is probably the most famous codebook design

algorithm. However, VQ still has its limitations. It usually generates

visible boundaries between blocks since the current block is coded

independently of its neighboring blocks. To deal with the above

problem, side match vector quantization (SMVQ) was proposed by

Kim in 1992 [65]. Kim successfully reduces the blocking effect by

using local edge information and provides better visual quality and

compression ratio than VQ does. Then, to make data hiding more

convenient, some researchers have tried to hide secret data in

cover images already compressed by VQ or SMVQ [66].

In [67], Lin et al. presented a method of embedding that was based

on VQ compressed images. The approach involves reducing the size

of the codebook and placing data in the remaining spaces of index

values. A codebook is first partitioned into two sub-codebooks such

that all pairs of the corresponding code vectors between sub

codebooks are as similar as possible. Any modification of the least


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significant bits of index values does not markedly distort the

reconstructed image, because the two sub codebooks have similar

content. Accordingly, secret data can be placed into the LSB of all

indices. In [68], Lu and Sun presented a similar method, but

extended to partition the codebook into 2k sub-codebooks for

embedding k bits into a single index.

VQ consists of three phases:

Codebook design

Encoding and

Decoding

Vector quantization can be defined as a mapping function that maps

k-dimensional vector space to a finite set CB = {C1, C2, C3, ..….,

CN}. The set CB is called codebook consisting of N number of

codevectors and each codevector Ci = {ci1, ci2, ci3, ……, cik} is of

dimension k. The key to VQ is the good codebook. Codebook can be

generated in spatial domain by using clustering.algorithms

Following codebook generation algorithms are used for data hiding.

Linde Buzo Gray (LBG)[64]

Kekre‟s Proportionate Error algorithm (KPE) [123]

Kekre‟s Median Codebook Generation algorithm (KMCG) [123]

Kekre‟s Fast Codebook Generation algorithm (KFCG) [125,

127]

Since the codebook is used for hiding data security is at a higher

level.


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2.1.2 Audio Files

(I)Overview of Human Auditory System (HAS)

Data hiding in audio signals is especially challenging as compared to

data hiding in digital images because the human auditory system

(HAS) operates over a wide dynamic range in comparison with

human visual system (HVS). Sensitivity to additive random noise is

also acute [31]. On the other hand, opposite to its large dynamic

range, HAS contains a fairly small differential range, i.e., loud

sounds generally tend to mask our weaker sounds [108].

Additionally, the HAS is unable to perceive absolute phase, only

relative phase [31]. Finally, there are some environmental

distortions so common as to be ignored by the listener in most of

the cases [31]. Such are the weaknesses of HAS that can be

exploited for hiding data in audio signals.

The effects of human auditory system (HAS) relative to

Steganography are temporal masking and frequency masking. In

temporal masking, a weaker audible signal on either side (pre and

post) of a strong masker becomes imperceptible. Similarly, in

frequency masking, if two signals occurring simultaneously are close

together in frequency, the stronger masking signal may make the

weaker signal inaudible [49].

(II)Methods of Audio Steganography

In the past few years, several algorithms for the embedding and

extraction of messages in audio sequences have been proposed. All

of the developed algorithms take advantage of the perceptual

properties (characteristics) of the human auditory system (HAS) in

order to hide data into the host signal in a perceptually transparent

manner. Some commonly used methods are [102]:

Least Significant Bit (LSB) Coding


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Parity Coding

Phase Coding

Spread Spectrum

Echo Data Hiding

(a)Least Significant Bit (LSB) Coding:

One of the earliest techniques studied in the information hiding of

digital audio (as well as other media types) is LSB coding. In this

technique, LSB of binary sequence of each sample of digitized audio

file is replaced with binary equivalent of secret message [102].

For example the letter „A‟ (binary equivalent 1000001) is to be

hidden into a digitized audio file where each sample is represented

with 16 bits, then LSB of 7 consecutive samples (each of 16 bit size)

is replaced with each bit of binary equivalent of the letter „A‟ [112]

as illustrated in Table 2.1.

Table 2.1 Example of Least Significant Bit (LSB) Coding

Sampled Audio Stream (16 bits)

‘A’ in binary

Audio stream with encoded message

1001 1000 0011 1100 1 1001 1000 0011 1101

1101 1011 0011 1000 0 1101 1011 0011 1000

1011 1100 0011 1101 0 1011 1100 0011 1100

1011 1111 0011 1100 0 1011 1111 0011 1100

1011 1010 0111 1111 0 1011 1010 0111 1110

1111 1000 0011 1100 0 1111 1000 0011 1100

1101 1100 0111 1000 1 1101 1100 0111 1001


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The number of LSB‟s for data hiding can be increased, but it also

increases the amount of resulting noise in the audio file as well.

Thus, one should consider the signal content before deciding on the

LSB operation to use. For example, a sound file that was recorded

in a bustling subway station would mask low-bit encoding noise. On

the other hand, the same noise would be audible in a sound file

containing a piano solo [102],[118]. To extract a secret message

from an LSB encoded sound file, the receiver needs access to the

sequence of sample indices used in the embedding process.

Advantages: (i) It is the simplest way to embed information in a

digital audio file. It allows large amount of data to be concealed

within an audio file, use of only one LSB of the host audio sample

gives a capacity equivalent to the sampling rate which could vary

from 8 kbps to 44.1 kbps (all samples used) [113]. (ii) It is widely

used as modification to LSB‟s usually does not create audible

changes to the sound files.

Disadvantages: (i) The Human ear is very sensitive and can often

detect even the slightest bit of noise introduced into a sound file.

(ii) It has considerably low robustness against attacks. For example,

if a sound file embedded with a secret message using LSB coding is

resampled, the embedded information would be lost [118].

(b)Parity Coding:

Instead of breaking a signal down into individual samples, the parity

coding method [102] breaks a signal down into separate regions of

samples and encodes each bit from the secret message in a sample

region's parity bit. If the parity bit of a selected region does not

match the secret bit to be encoded, the process flips the LSB of one

of the samples in the region. Thus, the sender has more of a choice


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in encoding the secret bit. Figure 2.1 illustrates how the first three

bits of the message 'HEY' are encoded with even parity.

Figure 2.1 Example of Parity Coding

The decoding process extracts the secret message by calculating

and lining up the parity bits of the regions used in the encoding

process. This method also has disadvantages similar to the LSB

coding method although the parity coding method does come much

closer in making the introduced noise inaudible.

(c)Phase coding:

Phase coding [102] is much more complex method than the

simplistic LSB encoding. Phase encoding “works by substituting the

phase of an initial audio segment with a reference phase that

represents the data. The phase of subsequent segments is adjusted

in order to preserve the relative phase between segments”. Phase


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coding relies on the fact that the phase components of sound are

not as perceptible to the human ear as noise is. Rather than

introducing perturbations, the technique encodes the message bits

as phase shifts in the phase spectrum of a digital signal, achieving

an inaudible encoding in terms of signal-to-perceived noise ratio.

Original and encoded signal are as shown in Figure 2.2. This method

is used when only a small amount of data, such as a watermark,

needs to be concealed.

Figure 2.2 The signals before and after Phase coding procedure

(d)Spread Spectrum (SS):

It attempts to spread out the encoded data across the available

frequencies as much as possible. This is analogous to a system

using an implementation of the LSB coding that randomly spreads

the message bits over the entire sound file. However, unlike LSB

coding, the Spread S method spreads the secret message over the

sound file‟s frequency spectrum, using a code that is independent of

the actual signal. As a result, the final signal occupies a bandwidth

in excess of what is actually required for transmission [102].

The SS method has the potential to perform better in some areas

than LSB coding, parity coding and phase coding techniques in that

it offers a moderate transmission rate while also maintaining a high


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level of robustness against attacks. However, it still has a

disadvantage that it can introduce noise into a sound file [102].

(e)Echo Data Hiding:

Text can be embedded in audio data by introducing an echo to the

original signal. the data is then hidden by varying three parameters

of the echo: initial amplitude, decay rate, and offset (delay time)

from the original signal [102]. If only one echo was produced from

the original signal, only one bit of information could be encoded.

Therefore, the original signal is broken down into blocks before the

encoding process begins. Once the encoding process is completed,

the blocks are concatenated back together to create the final signal.

For example, the original signal is divided up into blocks, and each

block is assigned a one or a zero based on the secret message. In

this case, the message is the binary equivalent of 'HEY' as shown in

Figure 2.3. First an echo signal is created from the entire original

signal using the binary zero offset value. Then a second echo signal

is created from the entire original signal using the binary one offset

value. Thus the "one" echo signal only contains ones, and the "zero"

echo signal only contains zeros. To combine the two echoes

together to get the final encoding, two mixer signals are used.

Figure 2.4 summarizes the implementation of the echo hiding

process.


40

Figure 2.3 An Example of Echo Hiding

Figure 2.4 Echo Hiding process

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