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International Journal of Electronics and Communication Engineering & Technology
(IJECET)
Volume 6, Issue 6, Jun 2015, pp. 09-17, Article ID: IJECET_06_06_002
Available online at
http://www.iaeme.com/IJECETissues.asp?JTypeIJECET&VType=6&IType=6
ISSN Print: 0976-6464 and ISSN Online: 0976-6472
© IAEME Publication
___________________________________________________________________________
AUDIO FORENSIC USING DWT-LSB
R. Karteek and Dr. A. S. Srinivasarao
Department of Electronics and Communication Engineering, AITAM,
Tekkali (AP), India
ABSTRACT
Many effective watermarking algorithms have been proposed and
implemented for digital images and digital video, however, few algorithms
have been proposed for audio watermarking. This is due to the fact that, the
human audio system is far more complex and sensitive than the human visual
system. In this paper, an audio watermarking algorithm based on DWT-LSB is
proposed. The DWT transformation decomposes the audio signal into several
sub bands, enabling developers to locate the most appropriate sub-bad to
embed. The watermark bits are embedded into the high-resolution sub-band
by using LSB algorithm, so the satisfactory robustness and the imperceptibility
performances are obtained
Key words: DWT, LSB, Forensic Mark, Imperceptibility and Robustness
Cite this Article: Karteek, R. and Dr. Srinivasarao, A. S. Audio Forensic
using DWT-LSB. International Journal of Electronics and Communication
Engineering & Technology, 6(6), 2015, pp. 09-17.
http://www.iaeme.com/IJECET/issues.asp?JTypeIJECET&VType=6&IType=6
_____________________________________________________________________
1. INTRODUCTION
By virtue of the new advancements in computer and telecommunication networks,
multimedia files are produced, stored and distributed easily across the globe.
However, the ownership and copyright of multimedia files are not usually protected.
Digital watermarking has been proposed in recent years as a means of protecting
multimedia contents from intellectual piracy. This is achieved by modifying the
original content, by inserting a signature which can be extracted, when necessary, as a
proof of ownership.
Generally the watermark can be obtained by two techniques: watermarking and
finger printing. Watermarking is the process of embedding marks in digital documents
(sounds, images, binaries, etc.) exactly like the watermarks on currency notes.
Fingerprinting is the process of embedding a serial number into every copy of an
object. This serial number can be used to detect the break of a license agreement. In
both cases, the embedded information is supposed to be invisible and very difficult to

2. R. Karteek and Dr. A. S. Srinivasarao
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remove it. The difference between the two processes is that in the former process the
all copies of object are all marked the same way, but in the latter process every copy
of object has a different embedded serial number [1].
Few algorithms are proposed for hiding data in audio files such as MP3Stego,
which effectively hides arbitrary information. The windows wave format lets users
hide data using stego-hide [2].
Data hidden can be classified according to the domain where the watermarking or
steganography has been applied. The following sections discuss these domains [3, 4]:
1.1. Time domain audio steganography
In time domain technology, watermark is directly embedded into the audio signal,
where there are no transformations applied. Watermark signal is shaped before it is
going to embed to ensure the robustness [5].
Hiding the watermark into time domain engages several challenges related to
robustness and inaudibility. Shaping the watermark before embedding, enables the
system to maintain the original audio signal quality and renders the watermark
inaudible [2].
1.2. Frequency domain audio steganography
In this technique, first the input signal is transformed to frequency domain and then
the watermark is added to the input signal. Later the inverse transform is applied to
the signal in order to get the watermarked signal [1113].
The most common transforms used are DWT (discrete wavelet transform), DFT
(discrete Fourier transform), DCT (discrete cosine transform). Examples of
techniques for this domain are LSB, SVD, Phase coding, Spread spectrum, Echo data
hiding [2, 6, 7].
2. REQUIREMENTS
A steganography system, in general, is expected to meet three key requirements,
namely, imperceptibility of embedding, accurate recovery of embedded information,
and large payload. An effective steganography scheme should possess the following
desired characteristics:
Secrecy: A person should not be able to extract the covert data from the host medium
without the knowledge of the proper secret key used in the extracting procedure.
Imperceptibility: The digital watermark should not affect the quality of original
audio signal after it is watermarked. The imperceptibility of an audio signal is
obtained by calculating the SNR of the audio signal. The SNR is obtained by taking
the difference in between the host audio signal and the forensic marked audio signal.
Robustness: A digital watermark is called robust if it resists a designated class of
transformations. Robust watermarks may be used in copy protection applications to
carry copy and no access control information [8].
High capacity: The maximum length of the covert message that can be embedded
should be as long as possible.
Resistance: The covert data should be able to survive when the host medium has been
manipulated, for example by some lossy compression scheme.
Accurate extraction: The extraction of the covert data from the medium should be
accurate and reliable.

3. Audio Forensic using DWT-LSB
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3. THE DISCRETE WAVELET TRANSFORM
Wavelets are special functions which, in a form analogous to sines and cosines in
Fourier analysis, are used as basal functions for representing signals. They provide
powerful multi-resolution tool for the analysis of non-stationary signals with good
time localization information. The coefficients of the discrete wavelet transform can
be calculated recursively and in a straight forward manner using the well-known
Mallat’s pyramid algorithm [14, 16]. Based on this algorithm, the one-dimensional
discrete wavelet coefficients of any stage can be computed from the coefficients of
the previous stage using the following iterative equations:
WL (n, j) = (1)
WH (n, j) = (2)
Where WL(n,j) is the nth
scaling coefficient at the jth
stage, WH(n,j) is the nth
wavelet
coefficient at the jth
stage, and h0(n) and h1(n) are the coefficients corresponding to the
scaling and wavelet functions. Equation (1) can then be used for obtaining the wavelet
coefficients of subsequent stages. In practice this decomposition is performed only for
a few stages. In order to reconstruct the original data, the discrete wavelet transform
coefficients are up-sampled and passed through another set of low pass and high
passfilters, which are expressed as:
∑ (3)
Where g0(n) and g1(n) are respectively the low-pass and high-pass synthesis filters
corresponding to mother wavelet. It is observed from Equation (3) that the jth
level
coefficients can be obtained from the (j+1)th
level coefficients.
Due to its excellent spatio-frequency localization properties, the DWT is very
suitable to identify areas in an audio signal where a watermark can be embedded
effectively. Some DWT-based audio watermarking techniques can be found in
literature [15, 17, 18].
4. LSB INSERTION TECHNIQUES
Low-bit encoding technique is considered as the earliest technique to add information
into digital audio signal. Low-bit encoding can be done by replacing the LSB of each
sample point by a coded string [2, 9].
In LSB insertion method, a random number generator is used to randomly
distribute and hide the bits of a secret message into the least significant bit of a cover
object, a common approach to achieve this is the random interval method. The
transmitting and receiving end share the stego key, the output is a random sequence
K1………..Kn where n is the length of message bits. The sequence is then used by the
sender to generate the sequence indices yi where,
y1 = K1 (4)
yi = yi-1 + Ki , i >2 (5)
Message bit, i would then be embedded into the LSB of the sample, yi and the
order in which the secret message bits are embedded would be determined pseudo
randomly. Since the receiver knows the seed k, he can reconstruct ki and therefore the
entire sequence of sample indices yi. In the random insertion method the random

4. R. Karteek and Dr. A. S. Srinivasarao
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location of the samples depends on a stego key, whose size k should be in the range n
< k < i. Where n is the size of message and i is the size of cover object. The method
commences by searching for the first prime number p that exceeds the key k, A
primitive root a, is then obtained, which is a number whose powers generate all the
distinct integers from 1 to (p-1) in some permuted order and distinct numbers, yi = ai
mod p, where i is the bit index of the secret message. Bit i of the secret message then
goes into LSB of pixel yi. In this way it is ensured that the bits of the secret message
are inserted into distinct LSBs.
4.1. Advantages of Low-bit encoding
1. High watermark channel bit rate
2. Low computational complexity
4.2. Disadvantage of Low-bit encoding
1. Low robustness, random changes of the LSB destroys the coded watermark.
2. The embedded watermark would survive analog to digital conversions and vice versa.
5. DWT BASED WATERMARKING
DWT based watermarking on audio signal has two procedures, one; watermarking
embedding and other; watermark extraction, as follows:
5.1. Algorithm for Watermark Embedding Process [10]
1. Consider the PCM encoded wave file.
2. Quantize the signal before it is applied DWT transformation.
3. Select the watermark text.
4. Convert watermark text into binary format.
5. Consider the high frequency part of the signal to perform LSB technique.
6. Start from a suitable position of the data bytes.
7. Edit the least significant bit with the data that have to be embedded.
8. Take every alternate sample and change the least significant bit to embed the whole
message. The data retrieving algorithm at the receiver’s end follows the same logic as
the embedding algorithm.
9. Apply IDWT transformation to get the Watermarked signal.
5.2. Algorithm for Watermark Extraction Process
1. Apply DWT to the Watermarked signal.
2. Leave the low frequency part and consider the high frequency part of the signal.
3. Apply inverse LSB on that to retrieve the hidden information.
4. Check every alternate sample and store the least significant bit in the previous queue
with a left shift of the previous bit.
5. Convert the binary values to decimal to get the ASCII values of the secret message.
6. From the ASCII find the secret message.
7. Apply IDWT to retain the original signal.

5. Audio Forensic using DWT-LSB
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6. RESULTS
To evaluate the performance of the DWT based audio watermarking algorithm, a
standard audio signal was used. A WAV file with 10 seconds of duration and was
taken sampled at 44.1 kHz width 16 bits per sample. The size of the text watermark
has taken into consideration after length of the audio signal is considered.
For different audio signals, the imperceptibility test was done (Figure 1 to 6,
shows original audio signal and corresponding audio signal after water marking
process) and the calculated SNR values are presented in Table 1. The SNR values are
calculated from:
Figure 1(a) Original ‘POP’ audio signal.
Figure 1(b) Forensic marked ‘POP’ audio signal.
Figure 2(a) Original ‘ROCK’ audio signal.
Figure 2(b) Forensic marked ‘ROCK’ audio signal.

6. R. Karteek and Dr. A. S. Srinivasarao
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Figure 3(a) Original ‘FOLKCOUNTRY’ audio signal.
Figure 3(b) Forensic marked ‘FOLKCOUNTRY’ audio signal.
Figure 4(a) Original ‘BLUE’ audio signal.
Figure 4(b) Forensic marked ‘BLUE’ audio signal.

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Figure 5(a) Original ‘FUNKSOULRNB’ audio signal.
Figure 5(b) Forensic marked ‘FUNKSOULRNB’ audio signal.
Figure 6(a) Original ‘RAPHIPPOP’ audio signal.
Figure 6(b) Forensic marked ‘RAPHIPPOP’ audio signal.
Table 1 SNR values for audio signals after water marking process.
Type of Signal SNR
POP 58.1317
ROCK 57.1049
FOLKCOUNTRY 56.6982
BLUE 58.7308
FUNKSOULRNB 53.7404
RAPHIPPOP 59.6167

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7. CONCLUSION
Audio watermarking is an active research area that has been derived as a solution to
copyright protection of digital audio products. Many audio watermarking techniques
have been proposed and proved as effective. However, due to the challenging nature
of audio signal processing, there remains much to do. In this paper, an audio
watermarking technique by implementing DWT transformation is proposed. The
spectrum of the host audio signal was decomposed to locate the most appropriate
region to embed the watermark bits, to achieve the robustness and imperceptibility.
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