Digital Audio Watermarking using Digital wavelet transform

Abstract:

Digital watermarking is a very recent research area. Digital audio watermarking is a method to embed or hide the Watermark (Information signal) into a digital signal i.e. Image, audio, text or video data. The watermark is difficult to remove from the audio signal. If the signal is copied, the information or watermark is also carried in the copy. A signal may carry several different watermarks at the same time. It is used to protecting multimedia data from unauthorized copying, piracy, ownership, inventions,

authentication etc. in this paper we present the watermarking methods and applications.

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, we describe an imperceptible and robust audio watermarking algorithm based on the discrete wavelet transform

Watermarking is a technique, which is used in protecting digital information like images, videos and audio as it provides copyrights and ownership. Audio watermarking is more challenging than image watermarking due to the dynamic supremacy of hearing capacity over the visual field.

Digital watermarking is a very recent research area. Digital audio watermarking is a method to embed or hide the Watermark (Information signal) into a digital signal i.e. Image, audio, text or video data. The watermark is difficult to remove from the audio signal. If the signal is copied, the information or watermark is also carried in the copy. A signal may carry several different watermarks at the same time. It is used to protecting multimedia data from unauthorized copying, piracy, ownership, inventions,

authentication etc. in this paper we present the watermarking methods and applications.

Introduction:Before the invention of steganography and cryptography, it was challenging to transfer secure information and, thus, to achieve secure communication environment [1]. Some of the techniques employed in early days are writing with an invisible ink, drawing a standard painting with some small modifications, combining two images to create a new image, shaving the head of the messenger in the form of a message, tattooing the message on the scalp and so on [15]. Normally an application is developed by a person or a small group of people and used by many. Hackers are the people who tend to change the original application by modifying it or use the same application to make profits without giving credit to the owner. It is obvious that hackers are more in number compared to those who create. Hence, protecting an application should have the significant priority. Protection techniques have to be efficient, robust and unique to restrict malicious users. The development of technology has increased the scope of steganography and at the same time decreased its efficiency since the medium is relatively insecure. This lead to the development of the new but related technology called „Watermarking‟. Some of the applications of watermarking include ownership protection, proof for authentication, air traffic monitoring, medical applications etc. [1] [5] [21]. Watermarking for audio signal has greater importance because the music industry is one of the leading businesses in the world [27]. 1.1 Background Globalization and internet are the main reasons for the growth of research and sharing of information. However, they have become the greatest tool for malicious user to attack and pirate the digital media. The watermarking technique during the evolution was used on images, and is termed as Image Watermarking. Image watermarking has become popular; however, the malicious user has started to extract the watermark creating challenges for the developers. Thus, developers have found another digital embedding source as audio and termed such watermarking

as Audio Watermarking. It is very difficult to secure digital information especially the audio and audio watermarking has become a challenge to developers because of the impact it has created in preventing copyrights of the music [12]. Note that it is necessary to maintain the copyright of the digital media, which is one form of intellectual property. Digital watermarking is a technique by which copyright information is embedded into the host signal in a way that the embedded information is imperceptible, and robust against intentional and unintentional attacks [14]. 1.2 Steganography and Watermarking 1.2.1 Steganography Steganography is evolved from the ancient technique known as the „Cryptography‟. Cryptography protects the contents of the message [15]. On the other hand, steganography is a technique to send information by writing on the cover object invisibly. Steganography comes from the Greek word that means covered writing (stego = covered and graphy = writing) [3]. Here the authorized party is only aware of the existence of the hidden message. An ideal steganographic technique conceals large amount of information ensuring that the modified object is not visually or audibly distinguishable from the original object. The steganography technique needs a cover object and message that is to be transported. It also requires a stego key to recover the embedded message. Users having the stego key can only access the secret message. Another important requirement for efficient steganographic

techniques is that, the cover object is modified in a way that the quality is not lost after embedding the message. 1.2.2 Watermarking Watermarking is a technique through which the secure information is carried without degrading the quality of the original signal. The technique consists of two blocks:

1.3 Differences between Steganography and Watermarking Although steganography and watermarking both describe techniques used for covert communication, steganography typically relates only to covert point to point communication between two parties [1]. Steganographic methods are not robust against attacks or modification of data that might occur during transmission, storage or format conversion [5]. Watermarking is one type of steganographic techniques whose primary objective is the security of the object rather than the invisibility of the object. The significant difference between the two techniques is the superior robustness capability of watermarking schemes [15]. To summarize, an ideal steganographic system can embed a large amount of information with no visible degradation to the cover object, but an ideal watermarking system would embed an amount of information that cannot be altered or removed without making the cover object entirely unusable. A watermarking system involves tradeoff between capacity and security [16]. 1.4 Image and Audio Watermarking Watermarking technique has evolved considerably from its origin [21]. Due to evolution of technology the medium of transmission has been changed. Watermarking is employed in digital media such as image and audio. The watermarking technique, in which the cover objects as discussed in Section 1.2.2, is image (audio) then the process is termed as Image (Audio) Watermarking. Audio watermarking is quite challenging than image watermarking due to the dynamic supremacy of human auditory system (HAS) over human visual system (HVS) [12]. 1.5 Applications of Watermarking

Ownership protection and proof of ownership: In ownership protection application, the watermark embedded contains a unique proof of ownership. The embedded information is robust and secure against attacks and can be demonstrated in a case of dispute of ownership. There can be the situations where some other person modifies the embedded watermark and claims that it is his own. In such cases the actual owner can use the watermark to show the actual proof of ownership [5] [18] [19].

Authentication and tampering detection: In this application additional secondary information is embedded in the host signal and can be used to check if the host signal is

tampered. This situation is important because it is necessary to know about the tampering caused to the media signal. The tampering is sometime a cause of forging of the watermark which has to be avoided [5] [18] [19].

Finger printing: Additional data embedded by a watermark in the fingerprinting applications are used to trace the originator or recipients of a particular copy of a multimedia file. The usage of an audio file can be recorded by a fingerprinting system. When a file is accessed by a user, a watermark, or called fingerprint in this case, is embedded into the file thus creating a mark on the audio. The usage history can be traced by extracting all the watermarks that were embedded into the file [7].

Broadcast monitoring: Watermarking is used in code identification information for an active broadcast monitoring. No separate broadcast channel is required as the data is

embedded in the host signal itself which is one of the main advantages of the technique [19].

Copy control and access control: A watermark detector is usually integrated in a recording or playback system, like in the DVD copy control algorithm [8] or during the development of Secure Digital Music Initiative (SDMI) [7]. The copy control and access control policy detects the watermark and it enforces the operation of particular hardware or software in the recording set [18].

Information carrier: The blind watermarking technique can be used in this sort of applications. These applications can transfer a lot of information and the robustness of the algorithm is traded with the size of content [15].

Medical applications: Watermarking can be used to write the unique name of the patient on the X-ray reports or MRI scan reports. This application is important because it is highly advisable to have the patients name entered on reports, and reduces the misplacements of reports which are very important during treatment [19].

Airline traffic monitoring: Watermarking is used in air traffic monitoring. The pilot communicates with a ground monitoring system through voice at a particular frequency. However, it can be easily trapped and attacked, and is one of the causes of miss communication. To avoid such problems, the flight number is embedded into the voice communication between the ground operator and the flight pilot. As the flight numbers are unique the tracking of flights will become more secure and easy [31].

Literature survey:AUDIO WATERMARKING TECHNIQUES − BACKGROUND This chapter provides the features of the human auditory system, which are important while dealing with the audio watermarking technique. Further, this chapter considers the requirement of an efficient watermarking strategy and different audio watermarking techniques involving both time and frequency domain. 2.1 Features of Human Auditory System (HAS) Note that audio watermarking is more challenging than an image watermarking technique due to wider dynamic range of the HAS in comparison with human visual system (HVS) [12]. Human ear can perceive the power range greater than 109: 1 and range frequencies of 103:1 [18]. In addition, human ear can hear the low ambient Gaussian noise in the order of 70dB [18]. However, there are some useful features such as the louder sounds mask the corresponding slow sounds. This feature can be used to embed additional information like a watermark. Further, HAS is insensitive to a constant relative phase shift in a stationary audio signal, and, some spectral distortions are interpreted as natural, perceptually non-annoying ones [12]. Two properties of the HAS dominantly used in watermarking algorithms are frequency (simultaneous) masking and temporal masking [13]:

Frequency masking: Frequency (simultaneous) masking is a frequency domain phenomenon where low levels signal (the maskee) can be made inaudible (masked) by a simultaneously appearing stronger signal (the masker), if the masker and maskee are close enough to each other in frequency [13]. A masking threshold can be found and is the level below which the audio signal is not audible. Thus, frequency domain is a good region to check for the possible areas that have imperceptibility.

Temporal masking: In addition to frequency masking, two phenomena of the HAS in the time domain also play an important role in human auditory perception. Those are pre-

masking and post-masking in time [13]. However, considering the scope of analysis in frequency masking over temporal masking, prior is chosen for this thesis. Temporal masking is used in application where the robustness is not of primary concentration. 2.2 Requirements of the Efficient Watermark Technique According to IFPI (International Federation of the Phonographic Industry) [19], audio watermarking algorithms should meet certain requirements. The most significant requirements are perceptibility, reliability, capacity, and speed performance [9].

Perceptibility: One of the important features of the watermarking technique is that the watermarked signal should not lose the quality of the original signal. The signal to noise ratio (SNR) of the watermarked signal to the original signal should be maintained greater than 20dB [19]. In addition, the technique should make the modified signal not perceivable by human ear.

Reliability: Reliability covers the features like the robustness of the signal against the malicious attacks and signal processing techniques. The watermark should be made in a way that they provide high robustness against attacks. In addition, the watermark detection rate should be high under any types of attacks in the situations of proving ownership. Some of the other attacks summarized by Secure Digital Music Initiative (SDMI), an online forum for digital music copyright protection, are digital-to-analog and analog-to-digital conversions, noise addition, band-pass filtering, time-scale modification, echo addition, and sample rate conversion [10].

Capacity: The efficient watermarking technique should be able to carry more information but should not degrade the quality of the audio signal. It is also important to know if the watermark is completely distributed over the host signal because, it is

possible that near the extraction process a part of the signal is only available. Hence, capacity is also a primary concern in the real time situations [19].

Speed: Speed of embedding is one of the criteria for efficient watermarking technique. The speed of embedding of watermark is important in real time applications where the embedding is done on continuous signals such as, speech of an official or conversation between airplane pilot and ground control staff. Some of the possible applications where speed is a constraint are audio streaming and airline traffic monitoring. Both embedding and extraction process need to be made as fast as possible with greater efficiency [19].

Asymmetry: If for the entire set of cover objects the watermark remains same; then, extracting for one file will cause damage watermark of all the files. Thus, asymmetry is also a noticeable concern. It is recommended to have unique watermarks to different files to help make the technique more useful [19]. 2.3 Problems and Attacks on Audio Signals As discussed in Section 2.2 the important requirements of an efficient watermarking technique are the robustness and inaudibility. There is a tradeoff between these two requirements; however, by testing the algorithm with the signal processing attacks that gap can be made minimal. Every application has its specific requirements, and provides an option to choose high robustness compensating with the quality of the signal and vice-versa. Without any transformations and attacks every watermarking technique performs efficiently. Some of the most common types of processes an audio signal undergoes when transmitted through a medium are as follows [11]:

Dynamics: The amplitude modification and attenuation provide the dynamics of the attacks. Limiting, expansion and compressions are some sort of more complicated

applications which are the non-linear modifications. Some of these types of attacks are re-quantization [20].

Filtering: Filtering is common practice, which is used to amplify or attenuate some part of the signal. The basic low pass and high pass filters can be used to achieve these types of attacks.

Ambience: In some situations the audio signal gets delayed or there are situations where in people record signal from a source and claim that the track is theirs. Those situations can be simulated in a room, which is of great importance to check the performance of an audio signal.

Conversion and lossy compression: Audio generation is done at a particular sampling frequency and bit rate; however, the created audio track will undergo so many different types of compression and conversion techniques. Some of the most common compression techniques are audio compression techniques based on psychoacoustic effect (MPEG and Advanced Audio Codec (AAC)). In addition to that, it is common process that the original audio signal will change its sampling frequencies like from 128Kbps to 64Kpbs or 48 Kbps. There are some programs that can achieve these conversions and perform compression operation. However, for testing purposes we have used MATLAB to implement these applications. Attacks like re-sampling and mp3 compression provide some typical examples.

Noise: It is common practice to notice the presence of noise in a signal when transmitted. Hence, watermarking algorithm should make the technique robust against the noise attacks. It is recommended to check the algorithm for this type of noise by adding the host signal by an additive white Gaussian noise (AWGN) to check its robustness.

In general, the time domain techniques provide least robustness as a simple low pass filtering can remove the watermark [20]. Hence time domain techniques are not advisable for the applications such as copyright protection and airline traffic monitoring; however, it can be used in applications like proving ownership and medical applications.

LITERATURE REVIEW

In recent years, various watermarking techniques have been proposed.. But all these techniques

can be used only for specific applications. Most of these techniques take the advantage of

perceptual properties of the human auditory system. Few techniques are discussed in the

literature. Xianghong Tang [10] has discussed audio watermarking algorithm based on DWT &

complex cestrum transform. This method embeds a binary image watermark into the complex

cepstral components of the important coefficients of the audio signal. This method achieves good

SNR about 39 dB & resists common signal processing attacks effectively. Pranab Kumar Dhar

[11] proposed a blind digital audio watermarking Technique based on discrete Fourier

Transform. In this method, input audio signal is divided into non-overlapping frames. The

magnitude and phase spectrum of each frame is calculated using discrete Fourier transform

(DFT)., Prominent peak is determined using a peak detection algorithm & watermark is

embedded in an audio which ensure that the watermark is located at the most significant

perceptual components of the audio. N. V. Lalitha [12] discussed a new Discrete wavelet

transform (DWT) & singular value decomposition based audio watermarking technique. The

technique based on DWT –SVD shows good robustness against most of the signal processing

opeartions. The proposed approach gives improved PSNR up to 69 dB & Mean opinion score

(MOS) value 5 as compared DCT-SVD based technique. Neha Baranwal & Kamalika Dutta [13]

presented a peak detection based spread spectrum audio watermarking using DWT. This

technique embeds watermark bits into the highest peak values of the third & fourth level detail

coefficients of Audio signal. As compared to the other existing techniques, this method shows

high SNR (15dB to 28 dB) & good robustness against signal processing attacks. Ali Al-Haj &

Ahmed Mohammad [14] proposed a robust watermarking approach using DWT & singular value

decomposition. Inaudibility & better robustness is achieved by embedding a binary image

watermark bits on the elements of singular values of the DWT sub bands of the audio frames.

This approach gives the improved SNR ranging from 21 dB to 25 dB as compared to traditional

Cox‟s & Bender‟s [1] methods. Although various watermarking algorithms are proposed but

these algorithms can be used for specific applications & not for any general application. Strong

robustness against attacks with high watermark embedding rate cannot be achieved

simultaneously.

Digital Audio Watermarking: An Overview

ISSN 2277-1956/V2N4-1231-1235

Transparency: Watermark should not affect the audio quality.

Robustness: Watermark should not detectable easily against attacks.

Capacity: Watermark should contain large information.

Fidelity: It means that the watermarked audio is same as original audio sound. The watermark

should be able to prevent

unauthorized detection, removal and embedding, unless the quality of audio becomes very poor.

Audio watermark embedding methods:

Time domain (Spatial domain) watermarking: According to this method the watermark is

embedded into original audio signal

using PN sequence noise generation which is added to audio [5]. This watermarking scheme is

easy to implement as compared to

transform domain schemes [1]. Least Significant Bit (LSB) technique is easy to implement to

embedding watermark. In LSB watermark is embedded into the audio by changing the least

significant bits of the audio samples. Watermark can be easilydamaged. Its capacity is low.

In phase coding technique the watermark is embedded having phase shift into phase spectrum of

audio [6]. This type of technique is more effective because phase shift cannot be audible by ear.

In echo-hiding technique, the watermark is embedded into audio signal by introducing an echo.

Spread-spectrum technique: This is a technique in which a signal is transmitted on a bandwidth

considerably larger than the frequency of the original information. In this technique, the bit of

secret information spreads over the audio frequency spectrum [7].

It requires different code for encoding as well as decoding of hidden information [5]. It is more

immune to noise. It also has

improved BER (Bit Error Rate) [8]. Direct Sequence Spread Spectrum (DSSS), Frequency

Hopping Spread Spectrum and Chirp

Spread Spectrum (CSS) techniques are used in this. CSS technique is most advantageous in

terms of power consumption, speed,

security and robustness [9].

Patch work technique: Patchwork is a data hiding technique developed by Bender et alii and

published on IBM Systems Journal,

1996. It is based on a pseudorandom, statistical model. It works by invisibly embedding a

specific statistic, with a Gaussian

distribution, into the host audio. Two sets of patches, of the audio. Patchwork has an extremely

low bit rate. Patchwork is its

resistance to cropping.

Transform domain (Frequency domain) watermarking: Transform domain uses several frequency

transforms i.e. Fast

Frequency Transform (FFT), Discrete Cosine Transforms and Discrete Wavelet Transforms and

others. In transform domain

watermarking information (watermark) embedded into the coefficients of audio signal. It is more

robust against signal processing

operations (Low pass filtering, sampling, quantization, compression). Transform domain

technique has large capacity for watermark

insertion. In contrast of robustness DWT technique is most effective [10].

Compressed domain watermarking: Audio compression a type of lossy or lossless compression

in which the amount of data in

a recorded waveform is reduced to differing extents for transmission respectively with or without some loss of quality, used in CD and MP3 encoding, Internet radio, and the like. In this technique the watermark is embedded into bit steam of host audio. No coding and decoding is required [11] [12]. The distortion becomes inaudible to ear.

PROPOSED WATERMARKING APPROACH:

The watermarking technique is divided into two blocks embedding and extraction. Embedding block is used to add the additional information into the host signal; whereas, extraction block is used to extract the watermark embedded in the audio signal. The watermark embedded is a binary image of dimension 64 × 64. A. Watermark Embedding procedure: The embedding process is divided into the individual blocks such as Watermark formation, wavelet decomposition, watermark embedding and reconstruction as shown in Fig. 4.

i) Wavelet decomposition:

Audio signal is decomposed into Haar wavelet filter. Four frequency sub-bands namely approximation, horizontal, vertical & diagonal bands. Low frequency coefficients of the decomposed signal are selected. Low frequency approximate wavelet coefficients are segmented into 2-D matrix blocks Dj, j=1, 2, 3………………M × N. Where M × N is the number of bits in the binary watermark image.

ii) Watermark embedding :

The watermark to be embedded is a binary image B of size M × N. The binary-image watermark is converted into a one dimensional vector. Embed the binary image watermark bits into the DWT transformed audio signal. After the watermark is embedded to the low frequency portion of the input signal, the portion can be recombined with the high pass filtered part to form the watermarked signal by applying inverse DWT operation. The obtained signal is the audio signal with watermark also termed as watermarked signal. F

B. The watermark extraction process:

The extraction process is illustrated in Fig. 5. The extraction algorithm without using original audio is described as follows;

Original adio signal

Wavelet decomposition

Watermark embedding

IDWT

Watermark Audio signal

Watermark Signal

Watermark audio signal

Matrix formation

IDWT

Watermark image

Original watermark

The extraction process is divided into blocks wave decomposition, Matrix formation watermark

extraction, Image formation. Watermark can be extracted from a watermarked signal by

procedure similar to the embedding process. The watermarked input is first decomposed by

discrete wavelet transform using Haar wavelet filter. The low frequency approximate wavelet

coefficients into 2-D matrix blocks. The low frequency component of the input is retrieved by

applying inverse wavelet transform to get audio signal. one dimensional watermark is

transformed into two dimensional watermark image & the watermark image is obtained.

experimental Evaluation test

Other than the subjective evaluation of the extracted watermark, robustness against attacks is

measured using Signal to Noise ratio (SNR), Peak signal to noise ratio[18], etc. Watermarked

signal may undergo common signal processing attacks such as resampling, re-quantization, low

pass filtering, Volume scaling, noise addition, etc. Although these attacks may not affect the

perceived quality of the watermarked audio but it may corrupt the watermark image embedded

within the host audio.

i) Signal to Noise Ratio (SNR) :

Signal to noise ratio gives the amount by which the signal is corrupted by the noise. It is defined

as the ratio of the signal power to the noise power. The SNR is widely utilized as an objective

measure of audio quality. It is simple to interpret, easy to apply. According to IFPI, when the

SNR is above 20 decibels (dB), audio watermarking should be imperceptible.. SNR can be

calculated by equation below. Where = Original audio Signal &= watermarked audio signal

10110log()()rSNRwiwidBN ()wi ()rwi

ii) Peal Signal to Noise Ratio (PSNR)

PSNR measures the estimates of the quality of the original & watermarked signal. It is related to mean square error (MSE).It is measured in decibels as 21025510logPSNRMSE MSE is the mean square error and defined as Where & are the original & the watermarked signals respectively with coordinate Position (i, j). *211()MNijijiJIIMSEMN ijI *ijI

Attacks: Volume Scaling /amplitude scalingAdditive Noise :

Additive white Gaussian noise (AWGN) is a basic noise model used in Information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics:

'Additive' because it is added to any noise that might be intrinsic to the information system.

'White' refers to idea that it has uniform power across the frequency band for the information system. It is an analogy to the color white which has uniform emissions at all frequencies in the visible spectrum.

'Gaussian' because it has a normal distribution in the time domain with an average time domain value of zero.

Low Pass Filtering

Re-sampling

Re-quantization

Applications of digital audio watermarking:

Content Identification: It is used for the proof of ownership and copyrights in music industry. Such type of watermark is hidden within the digital audio at the last stage of production of music files.

Ownership Protection: In the ownership protection applications, a watermark containing ownership information is embedded to the multimedia host signal. The watermark, known only to the copyright holder, is expected to be very robust and secure, enabling the owner to demonstrate the presence of this watermark in case of ownership.

Authentication and Tampering Detection: In the content authentication applications, a set of secondary data is embedded in the host multimedia signal and is later used to determine whether the host signal was tampered. The robustness against removing the watermark or making it undetectable is not a concern as there is no such motivation from attacker's point of view. However, forging a valid authentication watermark in an unauthorized or tampered host signal must be prevented. In general, the watermark embedding capacity has to be high to satisfy the need for more additional data than in ownership protection applications. The detection must be performed without the original host signal because either the original is unavailable or its integrity has yet to be established. This kind of watermark detection is usually called a blind detection.

Broadcasting: In this fast changing market, watermarking becomes critical for content owners, copyright holders, distributors and broadcasters. In a broadcast monitoring technique, special equipment is used to monitor and track T.V. channels or radio stations. During watermark detection, data are analyzed and broadcast details confirmed and reported.

Forensic: An agency inserts data into the audio which provides a way to identify the downloading user, distributor etc. The agency then extracts hidden information from the pirated copies. This is the real time application.

CONCLUSION

The watermarking technology has wide applications in the future of data

communication. The existing technologies like decoding are not very user

friendly and will soon be replaced by watermarking. Thus watermarking of audio

is going to be the most wanted technology in the internet enabled age and many

more new and advanced watermarking algorithms will be developed in the

future which will take the watermarking technology to newer heights.

In this paper, an effective data hiding technique for audio signal is proposed. It is observed that

the characteristic of DWT provides good tradeoff between the computational complexity and

robustness as compared to time domain technique. The performance of proposed method is

evaluated by the MOS, SNR, and PSNR. This approach is useful in copyright protection

applications in which the embedded information is related to the owner of the digital audio .

Performance improvement with respect to existing techniques can also be carried out.

Audio watermarking is an active research area that has been driven by the need to solve the

copyright protection problem of digital audio products. Many

promising audio watermarking techniques have been proposed and proved to be effective,

however, and due to the challenging nature of audio signal processing,

there remains much to do. In this paper, we proposed an effective audio signal watermarking

Algorithm based on the discrete wavelets transform. The

spectrum of the host audio signal was decomposed to locate the most appropriate regions to

embed the watermark bits, imperceptibly and robustly. Indeed, our simulation results

demonstrated the audibility and robustness of the proposed audio watermarking algorithm.

References:

[1] Acevedo A., Digital Watermarking for Audio Data in Techniques and

Applications of Digital Watermarking and Content Protection, Artech House,

USA, 2003.

[2] ADOBE® Audition, http://www.adobe.com/ products/audition/, 2009.

[3] Arnold M., “Audio Watermarking: Features, Applications and algorithms,” in

Processings of the IEEE International Conference on Multimedia and Expo, pp.

1013-1016, 2000.

[4] Arnold M., Wolthusen S., and Schmucker M.,“Techniques and Applications

of Digital Watermarking and Content Protection,” Artech House,

Psychoacoustics: Facts and models, Springer-Verlag, 2003.

[5] Bassia P. and Pitas I., “Robust Audio Watermarking in the Time Domain,”

IEEE Transactions on Multimedia, vol. 3, no. 2, pp.232-241, 2001.

[6] Beerends J. and Stemerdink J., “A Perceptual Audio Quality Measurement

Based on a Psychoacoustic Sound Representation,” Journal of the Audio

Engineering Society, vol. 40, no. 12, pp. 963-978, 1992.

[7] Bender W., Gruhl D., Morimoto N., and Lu A.,“Techniques for Data Hiding,”

IBM System Journal, vol. 35, pp. 313-336, 1996.

[8] Cox I., Kilian J., Leighton T., and Shamoon T.,“Secure Spread Spectrum

Watermarking for Multimedia,” IEEE Transactions on Image Processing, vol. 6,

no. 12, pp. 1673-1687. 1997.

[9] Cox I., Miller M., and Bloom J., “Digital Watermarking,” Academic Pressing,

USA, 2002.

[10] Cvejic N. and Seppänen T., “Increasing the Capacity of LSB-Based Audio

Steganography,” in Proceedings of the IEEE International Workshop on

Multimedia Signal Processing, pp. 336-338, 2002.

[11] Erelebi E. and Bataki L., “Audio Watermarking Scheme Based on

Embedding Strategy in Low Frequency Components with a Binary Image,”

Digital Signal Processing, vol. 19, no. 2, pp.265-277, 2009.

[12] Gruhl D., Lu A., and Bender W., “Echo Hiding,” in Proceedings of the

International Conference on Info Hiding, pp. 295-315, 1996.

[13] IFPI (International Federation of the Phonographic Industry),

http://www,ifpi.org, 2009.

[14] Katzenbeisser S. and Petitcolsd F., “Information Hiding Techniques for

Steganography and Digital Watermarking,” Artech House, Boston, USA, 2000.

[15] Kim H. and Choi Y., “A Novel Echo-hiding Scheme with backward and

Forward Kernels,” IEEE Transactions on Circuits and Systems for Video

Technology; vol. 13, no. 8, pp. 885-889, 2003.