This document evaluates the Two-Set algorithm for embedding a watermark into audio signals. The algorithm works in the frequency domain using the discrete cosine transform. 100 audio clips across 5 genres were watermarked. Inaudibility was tested by calculating objective difference grades, finding the watermarks were slightly perceptible. Robustness was tested by resampling and adding white Gaussian noise. The watermark withstood resampling up to 88.2kHz and noise up to 5dB signal-to-noise ratio. Pop and banda music showed most robustness, while electronic and ballads showed least. The study found the Two-Set algorithm can embed imperceptible watermarks and demonstrated robustness against common attacks.
Quality and Distortion Evaluation of Audio Signal by SpectrumCSCJournals
Information hiding in digital audio can be used for such diverse applications as proof of ownership, authentication, integrity, secret communication, broadcast monitoring and event annotation. To achieve secure and undetectable communication, stegano-objects, and documents containing a secret message, should be indistinguishable from cover-objects, and show that documents not containing any secret message. In this respect, Steganalysis is the set of techniques that aim to distinguish between cover-objects and stegano-objects [1]. A cover audio object can be converted into a stegano-audio object via steganographic methods. In this paper we present statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stegano-audio signals vis-à-vis their de-noised versions, are statistically different. A distortion metric based on Signal spectrum was designed specifically to detect modifications and additions to audio media. We used the Signal spectrum to measure the distortion. The distortion measurement was obtained at various wavelet decomposition levels from which we derived high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. This paper looking at evidence in a criminal case probably has no reason to alter any evidence files. However, it is part of an ongoing terrorist surveillance might well want to disrupt the hidden information, even if it cannot be recovered
A Survey on Video Watermarking Technologies based on Copyright Protection and...Editor IJCATR
Digital Watermark is class of marker or symbol secretly embedded in a multimedia signal such as Audio, Image or Video. It
is used to identify the ownership of the multimedia signal. Video watermarking is an emerging area for various applications like copy
control broadcast monitoring, video authentication, copyright protection and enhanced video coding. The main objective of this paper
is to present survey and comparisons of various available techniques on video watermarking based on copyright protection and
identification. Comparative study of various technologies gives the significant information about the PSNR, payload, quality factor
and also the various attacks used in video watermarking techniques. The best techniques in various scenarios are discussed in this
paper which will help the research scholars in field of video watermarking.
Quality and Distortion Evaluation of Audio Signal by SpectrumCSCJournals
Information hiding in digital audio can be used for such diverse applications as proof of ownership, authentication, integrity, secret communication, broadcast monitoring and event annotation. To achieve secure and undetectable communication, stegano-objects, and documents containing a secret message, should be indistinguishable from cover-objects, and show that documents not containing any secret message. In this respect, Steganalysis is the set of techniques that aim to distinguish between cover-objects and stegano-objects [1]. A cover audio object can be converted into a stegano-audio object via steganographic methods. In this paper we present statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stegano-audio signals vis-à-vis their de-noised versions, are statistically different. A distortion metric based on Signal spectrum was designed specifically to detect modifications and additions to audio media. We used the Signal spectrum to measure the distortion. The distortion measurement was obtained at various wavelet decomposition levels from which we derived high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. This paper looking at evidence in a criminal case probably has no reason to alter any evidence files. However, it is part of an ongoing terrorist surveillance might well want to disrupt the hidden information, even if it cannot be recovered
A Survey on Video Watermarking Technologies based on Copyright Protection and...Editor IJCATR
Digital Watermark is class of marker or symbol secretly embedded in a multimedia signal such as Audio, Image or Video. It
is used to identify the ownership of the multimedia signal. Video watermarking is an emerging area for various applications like copy
control broadcast monitoring, video authentication, copyright protection and enhanced video coding. The main objective of this paper
is to present survey and comparisons of various available techniques on video watermarking based on copyright protection and
identification. Comparative study of various technologies gives the significant information about the PSNR, payload, quality factor
and also the various attacks used in video watermarking techniques. The best techniques in various scenarios are discussed in this
paper which will help the research scholars in field of video watermarking.
AUDIO VOICE RECORDER AND TRANSMISSION THROUGH A WIRELESS MEDIUM BETWEEN PC AN...wadzani umaru
There is a fast growing need to support audio voice communication in wireless channel between a voice source and personal computer PC. In this paper we develop an algorithm using matlab software to indicate how a PC can receive audio voice signal from an audio source, record it and plot the resulting waveform on a graphical representation. The
actual data rate for audio voice transmission over IP is 64000bps, which was used as medium for audio voice communication between a PC and audio voice source
Ijaems apr-2016-30 Digital Audio Watermarking using EMD for Voice Message Enc...INFOGAIN PUBLICATION
Several accurate watermarking methods for image watermarking have being suggested and implemented to secure various forms of digital data, images and videos however, very few algorithms are proposed for audio watermarking. This is also because human audio system has dynamic range which is wider in comparison with human vision system. In this paper, a new audio watermarking algorithm for voice message encryption based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each frame is then decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). The watermark, which is the secret message that is to be sent, along with the synchronization codes are embedded into the extrema of the last IMF, a low frequency mode stable under different attacks and preserving the perceptual quality of the host signal. Based on exhaustive simulations, we show the robustness of the hidden watermark for audio compression, false decryption, re-quantization, resampling. The comparison analysis shows that our method has better performance than other steganography schemes recently reported.
A Novel Digital Watermarking Technique for Video Copyright Protection cscpconf
Due to the rapid growth of internet and technology, protecting digital data is becoming very
urgent. In this paper a novel watermarking technique is proposed where both visible and invisible watermarks are embedded in a video. Digital data can be copied easily without any degradation in quality, so the protection of the data is necessary. Digital watermarking is a technology to embed additional information into the host signal to ensure security and protection of multimedia data. The video frames contain both the watermarks, so it is more robust to attacks. The watermarking scheme described here deals with embedding and extraction of the watermarks. Discrete Wavelet transform (DWT) is used to embed the invisible watermark and Peak Signal to Noise Ratio (PSNR) is calculated to measure efficiency of this method.
Wireless digital communication and coding techniques newClyde Lettsome
Lecture about some modern digital communication techniques in this lecture. These techniques will include but are not limited to:
- Code Error Detection and correction
- Parity
- Cyclical Redundancy Coding (CRC)
- Hamming Code
- Digital Modulation Techniques
- Frequency Shift Keying (FSK)
- Binary Phase Shift Keying (BPSK)
- Quadrature Phase Shift Keying (QPSK)
- Channel Access
- Time Division Multiple Access (TDMA)
- Code Division Multiple Access (CDMA)
Audio Essentials for Broadcast and MultiscreenEllis Reid
This wall chart highlights the key terms and standards required for the delivery of premium audio across broadcast and multiscreen workflows. It is designed as a quick reference for people who are responsible for delivering rich media experiences acress broadcast and over the top networks
DWT Based Audio Watermarking Schemes : A Comparative Study ijcisjournal
The main problem encountered during multimedia transmission is its protection against illegal distribution
and copying. One of the possible solutions for this is digital watermarking. Digital audio watermarking is
the technique of embedding watermark content to the audio signal to protect the owner copyrights. In this
paper, we used three wavelet transforms i.e. Discrete Wavelet Transform (DWT), Double Density DWT
(DDDWT) and Dual Tree DWT (DTDWT) for audio watermarking and the performance analysis of each
transform is presented. The key idea of the basic algorithm is to segment the audio signal into two parts,
one is for synchronization code insertion and other one is for watermark embedding. Initially, binary
watermark image is scrambled using chaotic technique to provide secrecy. By using QuantizationIndex
Modulation (QIM), this method works as a blind technique. The comparative analysis of the three methods
is made by conducting robustness and imperceptibility tests are conducted on five benchmark audio
signals.
Survey on Different Methods of Digital Audio WatermarkingIJERA Editor
The significant progress of the technology gives the full access to the digital data for retransmitting and reproduction with comfort. Since the benefits of such progress is easily available, they equally immune to some illegal manipulation of data. So there is necessity arises for the protection of digital data from unauthorized users. The digital audio watermarking technique is new technology among different watermarking techniques which provides successful solutions to problems occurred from some digital attacks. Basically watermarking is the scheme in which binary information is embedded into the original signal. The major concern of the audio watermarking scheme is to provide the proof of ownership to the owner and to provide protection for embedded data. This paper provides concise analysis of different existing audio water.
AUDIO VOICE RECORDER AND TRANSMISSION THROUGH A WIRELESS MEDIUM BETWEEN PC AN...wadzani umaru
There is a fast growing need to support audio voice communication in wireless channel between a voice source and personal computer PC. In this paper we develop an algorithm using matlab software to indicate how a PC can receive audio voice signal from an audio source, record it and plot the resulting waveform on a graphical representation. The
actual data rate for audio voice transmission over IP is 64000bps, which was used as medium for audio voice communication between a PC and audio voice source
Ijaems apr-2016-30 Digital Audio Watermarking using EMD for Voice Message Enc...INFOGAIN PUBLICATION
Several accurate watermarking methods for image watermarking have being suggested and implemented to secure various forms of digital data, images and videos however, very few algorithms are proposed for audio watermarking. This is also because human audio system has dynamic range which is wider in comparison with human vision system. In this paper, a new audio watermarking algorithm for voice message encryption based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each frame is then decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). The watermark, which is the secret message that is to be sent, along with the synchronization codes are embedded into the extrema of the last IMF, a low frequency mode stable under different attacks and preserving the perceptual quality of the host signal. Based on exhaustive simulations, we show the robustness of the hidden watermark for audio compression, false decryption, re-quantization, resampling. The comparison analysis shows that our method has better performance than other steganography schemes recently reported.
A Novel Digital Watermarking Technique for Video Copyright Protection cscpconf
Due to the rapid growth of internet and technology, protecting digital data is becoming very
urgent. In this paper a novel watermarking technique is proposed where both visible and invisible watermarks are embedded in a video. Digital data can be copied easily without any degradation in quality, so the protection of the data is necessary. Digital watermarking is a technology to embed additional information into the host signal to ensure security and protection of multimedia data. The video frames contain both the watermarks, so it is more robust to attacks. The watermarking scheme described here deals with embedding and extraction of the watermarks. Discrete Wavelet transform (DWT) is used to embed the invisible watermark and Peak Signal to Noise Ratio (PSNR) is calculated to measure efficiency of this method.
Wireless digital communication and coding techniques newClyde Lettsome
Lecture about some modern digital communication techniques in this lecture. These techniques will include but are not limited to:
- Code Error Detection and correction
- Parity
- Cyclical Redundancy Coding (CRC)
- Hamming Code
- Digital Modulation Techniques
- Frequency Shift Keying (FSK)
- Binary Phase Shift Keying (BPSK)
- Quadrature Phase Shift Keying (QPSK)
- Channel Access
- Time Division Multiple Access (TDMA)
- Code Division Multiple Access (CDMA)
Audio Essentials for Broadcast and MultiscreenEllis Reid
This wall chart highlights the key terms and standards required for the delivery of premium audio across broadcast and multiscreen workflows. It is designed as a quick reference for people who are responsible for delivering rich media experiences acress broadcast and over the top networks
DWT Based Audio Watermarking Schemes : A Comparative Study ijcisjournal
The main problem encountered during multimedia transmission is its protection against illegal distribution
and copying. One of the possible solutions for this is digital watermarking. Digital audio watermarking is
the technique of embedding watermark content to the audio signal to protect the owner copyrights. In this
paper, we used three wavelet transforms i.e. Discrete Wavelet Transform (DWT), Double Density DWT
(DDDWT) and Dual Tree DWT (DTDWT) for audio watermarking and the performance analysis of each
transform is presented. The key idea of the basic algorithm is to segment the audio signal into two parts,
one is for synchronization code insertion and other one is for watermark embedding. Initially, binary
watermark image is scrambled using chaotic technique to provide secrecy. By using QuantizationIndex
Modulation (QIM), this method works as a blind technique. The comparative analysis of the three methods
is made by conducting robustness and imperceptibility tests are conducted on five benchmark audio
signals.
Survey on Different Methods of Digital Audio WatermarkingIJERA Editor
The significant progress of the technology gives the full access to the digital data for retransmitting and reproduction with comfort. Since the benefits of such progress is easily available, they equally immune to some illegal manipulation of data. So there is necessity arises for the protection of digital data from unauthorized users. The digital audio watermarking technique is new technology among different watermarking techniques which provides successful solutions to problems occurred from some digital attacks. Basically watermarking is the scheme in which binary information is embedded into the original signal. The major concern of the audio watermarking scheme is to provide the proof of ownership to the owner and to provide protection for embedded data. This paper provides concise analysis of different existing audio water.
DIGITAL WATERMARKING TECHNIQUE BASED ON MULTI-RESOLUTION CURVELET TRANSFORMijfcstjournal
In this paper an efficient & robust non-blind watermarking technique based on multi-resolution geometric analysis named curvelet transform is proposed. Curvelet transform represent edges along curve much more efficiently than the wavelet transform and other traditional transforms. The proposed algorithm of embedding watermark in different scales in curvelet domain is implemented and the results are compared
using proper metric. The visual quality of watermarked image, efficiency of data hiding and the quality of extracted watermark of curvelet domain embedding techniques with wavelet Domain at different number of decomposition levels are compared. Experimental results show that embedding in curvelet domain yields best visual quality in watermarked image, the quality of extracted watermark, robustness of the watermark and the data hiding efficiency.
Digital Watermarking Technique Based on Multi-Resolution Curvelet Transform ijfcstjournal
In this paper an efficient & robust non-blind watermarking technique based on multi-resolution geometric
analysis named curvelet transform is proposed. Curvelet transform represent edges along curve much more
efficiently than the wavelet transform and other traditional transforms. The proposed algorithm of
embedding watermark in different scales in curvelet domain is implemented and the results are compared
using proper metric. The visual quality of watermarked image, efficiency of data hiding and the quality of
extracted watermark of curvelet domain embedding techniques with wavelet Domain at different number of
decomposition levels are compared. Experimental results show that embedding in curvelet domain yields
best visual quality in watermarked image, the quality of extracted watermark, robustness of the watermark
and the data hiding efficiency.
The embedding of a digital signature, or tag data is carried out in the frequency domain. The
high frequency varieties are chosen by any LH and HL in the wavelet domain which are to be
applicable in DCT. Coefficients are changed mid-frequency DCT coefficients such transactions by a
low frequency of the watermark to be embedded. Watermark can be recovered from the video by
selecting a random watermark of any reference framework. The proposed techniques are more
secure, robust and are efficient due to the use of static DCT. Watermark techniques uses a bands HL
and LH for adding watermark where the movement does not impact the quality the extracted
watermark until if the video displays for different types of malware attacks.
In this work we have taken three video watermarking techniques i.e. BIT GET (spatial),
DWT, DCT and one video formats ie.MPEG video to perform a comparative analysis of different
techniques using single video formats, to obtain the best performing technique for video
watermarking. Such that to increase robustness of the video and decrease the embedding time
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Comparison of Wavelet Watermarking Method With & without Estimator Approachijsrd.com
In this paper we propose an Estimator approach with wavelet watermarking method which is capable to hide watermark in the host image based on wavelet domain technique. Using the Estimator approach the proposed technique becomes robust against different noise attacks. For the evaluation of Imperceptibility & Robustness of the proposed method we have calculated basic statistical parameters. We have tested watermarked image against different noise attacks at different noise densities. Due to the use of estimator the perceptible quality of extracted image is better though the image is degraded by high density noise.
A Hybrid DWT-SVD Method for Digital Video Watermarking Using Random Frame Sel...researchinventy
Digital watermarking refers to embedding watermarks in a multimedia documents and files in order to protect them from illegal copying and identifying manipulations. We proposed a technique is Hybrid DWTSVD method for digital video watermarking. This paper presents a robust and secure watermarking algorithm for video sequence. Proposed scheme divide the video in to frames and then blue channel is used for watermarking insertion. Discrete wavelet transform is used for watermark embedding. Watermark is inserted in to mid frequency component for better resistance to video manipulation operation. PSNR and MSE are computed for testing the proposed method
Comparative Study of Various Algorithms for Detection of Fades in Video Seque...theijes
In the multimedia environment, digital data has gained more importance in daily routine. Large volume of videos such as entertainment video, news video, cartoon video, sports video is accessed by masses to accomplish their different needs. In the field of video processing Shot boundary detection is current research area. Shot boundary detection has vast impact on effective browsing and retrieving, searching of video. It serves as the beginning to construct the content of videos. Video processing technology has crucial job to provide valid information from videos without loss of any information. This paper is a survey of various novel algorithm for detecting fade-in and fade-out used by renowned personals with different methods. This survey also emphasizes on different core concepts underlying the different detection schemes for the most used video transition effect: fades
Performance Analysis of Digital Watermarking Of Video in the Spatial Domainpaperpublications3
Abstract:In this paper, we have suggested the spatial domain method for the digital video watermarking for both visible and invisible watermarks. The methods are used for the copyright protection as well as proof of ownership. In this paper we first extracted the frames from the video and then used spatial domain characteristics of the frames where we directly worked on the pixel value of the frame according to the watermark and calculated different parameters.
In this presentation both the major domains of information security is explored.
1) Watermarking
2) Steganography
factors affecting them,applications,various techiniques are discussed in the presentation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
1. International Journal of Research in Engineering and Science (IJRES)
ISSN (Online): 2320-9364, ISSN (Print): 2320-9356
www.ijres.org Volume 3 Issue 11 ǁ November. 2015 ǁ PP.40-48
www.ijres.org 40 | Page
Evaluation of Two-Set Algorithm in Embedding a Watermark
into an Audio Signal
C. Aquino1
,E. Perez2
, C. Aguilar3
1
esime Culhuacan, National Polytechnic Institute.
2
esime Culhuacan, National Polytechnic Institute.
3
esime Culhuacan, National Polytechnic Institute.
Abstract: The protection of multimedia data has become a necessity due to the generation of illegal copies,
thus, the purpose of this article was to analyze and evaluate the algorithm of two-set in embedding a watermark
into an audio signal. Metrics were analyzed as the Perceptual Evaluation of Audio Quality (PEAQ) by
calculating the Objective Difference Grade (ODG) to verify the inaudibility of the watermark and the robustness
of it against attacks from resampling and Additive White Gaussian Noise (AWGN) by estimating the Bit Error
Rate (BER). The process was applied to hundred Waveform Audio Format (WAV) clips classifieds in five
musical genders and we obtained an ODG value average of -0.83045 that according to the International
Telecommunication Union (ITU), the watermark is slightly perceptible if you listen with extreme care.
I. Introduction
Digital media has become a daily task, since these are new services required by people today, due to the
exchange of audio, images, text and video. The problem lies when these files are copied anddistributed without
permission of the author. To solve this problem there is digital watermarking, a technique to embed data in the
signal to be protected (in this case audio) as an identifier. There are several algorithms to insert watermarks for
images and video, however, few algorithms are dedicated to audio files because of the complexity of the Human
Auditory System (HAS)[1], hence the purpose of this research is to insert a watermark into audio signals,
interpret and evaluate the Two-Set algorithm using modified patchwork and verify the inaudibility and
robustness of the watermark to be used as an identifier of the copyright.
The watermark protects files permanently embedded in the audio signal so that the audio quality is not
impaired while ensuring that the watermark is imperceptible. On the other hand, the watermark should be robust
against attacks or manipulations that are carried out, at one point being the most common is resampling the
addition of noise and more to the original signal. Hardly a watermark can be extracted without affecting the
original signal; this creates a very attractive tool for copy protection[2]. Figure 1 shows the basic scheme of
embedding and detecting a watermark.
2. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 41 | Page
Figure1. Watermark embedding and detecting scheme
The embedding process is performed by the Modified Patchwork technique3] equations derived from the
original Patchwork method to increase the robustness (but it should be emphasized that this derivation is not
enough to be resistant to many attacks, so it should be possible to combine different watermarking methods),
working in the frequency domain under the Discrete Cosine Transform (DCT) and the detection process by
Inverse Discrete Cosine Transform (IDCT) and it is possible with a secret key.
Section 2 shows the mathematical development of the algorithm, divided in the embedding process, metrics
evaluation and detection process. In Section 3, the method is implemented, it shows the initial parameters and
comparing the original signal and the watermarked and checking the results of the PEAQ between different
types of audio files tagged and thus verify the inaudibility of the watermark embedded in them, furthermore, it
shows the attacks that the watermark survives and its corresponding BER. The conclusions are in Section 4 and
finally in Section 5 of the references consulted.
II. Development
The evaluation of the method was built of three parts: the first is the embedding of the watermark in the
audio signal; the second part is inaudibility tests and robustness to audio clips marked, finally it detects the
embedded watermark after attacks.
The authors of [3] proposed watermark embedding method is known as Two-Set, based on modified
patchwork algorithm. It works in the frequency domain and is based on the calculation of the DCT.
2.1. Watermark embedding
The watermark insertion steps[4] are summarized below:
The secret key and the digital watermark are associated to the seed of a random number generatorDivides the
audio file into blocks of size Nand DCT is applied to each block.
We define two subsets of the set processing, one called A and another B
(1)
(2)
Sample means and the statistical standard error are calculated according to the following equations:
3. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 42 | Page
(3)
(4)
(5)
The embeddingfunctions presented below introduce the watermark:
(6)
(7)
Where C is a constant and sign is the sign function what makes large values larger and small values smaller.
Finally, replace the selected itemsandrespectively apply the IDCT in theFrequency domain
2.2. Metrics evaluation
The evaluation metrics are to test the inaudibility of the watermark embedded in the audio clips and check
the robustness of it against attacks from resampling and AWGN.
Inaudibility test should evaluate the quality of the audio clips watermarked with the standard ITU BS.1387-
1[7] established by ITU for PEAQ provides accurate estimates of audio quality degradation occurring in the
watermarked signal compared with the original. This is based on the properties of the human auditory system,
modeling the psychoacoustic effects and is verified by calculating the Objective Difference Grade (ODG). The
ODG ranges from 0 to -4 and is defined as Table 1
Table 1. Odg values range
Impairment Description ODG
Imperceptible 0.0
Perceptible, but not annoying -1.0
Slightly annoying -2.0
Annoying -3.0
Very annoying -4.0
For robustness tests we resampled the signals watermarked by different sampling rates. This process was
done by changing the sampling frequency of thesignal watermarked and checking that the embedded bits were
the same in the detection process, it was calculated how many erroneous bits exist in the watermarked audio
clip, it is the number of bit errors divided by the total number of transferred bits during a studied time interval
and its correspondent BER [6] as a unit less performance measure, often expressed as a percentage.
Also, it was added an AWGN to the watermarked clips. The AWGN term refers to the fact that eventually
the noise is combined with the desired signal. As the noise gets added (and not multiplied) to the received
signal, the spectrum noise if flat for all frequencies and the values of the noise follows the Gaussian
probability distribution function:
(8)
Where;
(9)
(10)
It was verified that the watermark survives the AWGN; this was done by calculating the BER in order to
4. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 43 | Page
obtain the number of erroneous bits that exist in different intensities of Signal to Noise Ratio (SNR) of the
AWGN. Figure 2 shows the general outline of the AWGN, note that it is additive and not multiplicative
Figure 2. Awgn scheme
2.3. Watermark detection
The following algorithm[4] was used to verify whether the embedded watermark was the same in the
discovery process after the attacks. The decoding steps were as follows:
The secret key and the digital watermark are associated to the seed of a random number generator.
Obtain subsets y for bit insertion 0 and thesubsets
y for bit insertion 1.
Averages are calculated for each sample subsets and standard errors for 0 y 1.
We calculate test statistics S (see equation 7) andT2
(11)
T2
is defined as the highest values of the statisticsobtained.
The seed detected will be that for the parameter T2
is maximum.
Finally we generate the watermark associated with the seed, getting the embedded bits.
5. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 44 | Page
The process was to watermark audio clips;subject to attacks and to detect the survival of the watermark. Below
are the results of the evaluation algorithm.
III. Results
The algorithm was applied to a hundred audio clips of music (of which only twenty are reported) divided in
five categories, these files were WAV, the watermark embedded was W=[1 0 0 0 0 1 1 0]. First we compared
the original signals with the watermarked to verify that no significant alteration in the watermarked clips. Figure
3 shows the spectrum of an audio signal(watermarked and un-watermarked) and we notice a slight difference
between the two graphs.
Figure 3. Spectrum of an audio clip, original and watermarked
The red color in the first graph shows the original signal and the second graph the red
representswatermarked signal, note a difference between the two tones.
Figure 4 plots the difference between the originalsignal and the watermarked signal.
Figure 4. Difference watermarked and un-watermarked signals
Note that the difference between the two signals is the order of 10-5
.
To demonstrate the inaudibility of the embedded watermark, we performed the calculation of ODG for each
audio clip, which are shown in Table 2.
6. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 45 | Page
Table 2. Odg of the audio clips
Category Track name ODG
Dopamina (by Belinda) -0.753
Contigo (by Yuridia) -0.895
Pop
Nada puede cambiarme
(by Paulina Rubio) -0.101
MentirasPiadosas
(by Alejandra Guzmán) -0.832
Las pequeñas
-2.761
cosas (by Jot Dog)
Lentamente (Fey) -1.676
Electronic Nada de más (by Belanova) -2.183
Provócame (by Fey) -1.752
Y tú (by Julión Álvarez) -0.166
Mujeresdivinas (by Vicente
Banda Fernández) -0.108
No me quedamás(by Selena) -0.126
Ya no (by Limon) -0.178
Note that the difference between the two signals is the order of 10
-5
.
To demonstrate the inaudibility of the embedded watermark, we performed the calculation of ODG for each
audio clip, which are shown in Table 2.
Table 2. Odg of the audio clips
Chiquitita (by Abba) -0.419
Cantacorazón (by
Alejandro Fernández) -0.610
Ballad
Sortilegio (by Il divo) -0.291
Hasta mi final (by
-0.523
Il divo)
I feel good (by
-1.361
James Brown)
Absolutamente
-0.594
(by Fangoria)
Rock
Lejosestamos
-0.431
mejor (by Motel)
Frágil (by Allison) -0.849
The ODG average value obtained was -0.83045 for twenty clips above, this result indicates that the
watermark is slightly perceptible only if you listen carefully but it is not annoying to the human ear.
7. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 46 | Page
Comparing the hundred ODG values with Table 1 we obtain Table 3 shows the percentage obtained for
each impairment established by the ITU.
Table 3. Obtained percent of the odg
We could see that most of the embedded watermarks are to be imperceptible. Figure 5 shows the average
ODG for each musical category
Average Objective DifferenceGrade
Figure 5. Average odg for each category
Table 4. Ber for each resampling rate
Category Sampling Erroneous Bit
Frequency Bits / Total Error
(kHz) Embedded Rate
Bits (%BER)
22.05 1/8 12.5
88.2 0/8 0
Pop 176.4 0/8 0
192 2/8 25
22.05 2/8 25
Electronic 88.2 1/8 12.5
176.4 2/8 25
192 3/8 37.5
22.05 0/8 0
88.2 0/8 0
Banda 176.4 0/8 0
192 1/8 12.5
22.05 1/8 12.5
88.2 0/8 0
Ballad 176.4 1/8 12.5
192 2/8 25
22.05 1/8 12.5
88.2 0/8 0
Rock 176.4 0/8 0
192 2/8 25
Impairment Obtained
Description Percent (%)
Imperceptible 52.5
Perceptible, but not
23
annoying
Slightly annoying 19.5
Annoying 5
Very annoying 0
8. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 47 | Page
The watermark is more perceptible in the electronic music (Figure 5).
To check the robustness of the watermark[5], we subjected the processed signal to an attack by changing
the resampling rate as shown in Table 4 and AWGN attack with different intensities of Signal to Noise Ratio
(SNR) showing the results Table 5.
Table 4 shows that most of the clips watermarkedsuffer a distortion in the embedded watermark when
resampled to 192kHz and we said that the larger the resampling rate, the greater the distortion of the watermark
embed it on audio clips.
The watermark, subject to resampling, was more robust in banda music, pop and rock, and less robust in
electronic music and ballads.
In the following table we can see the number of bit errors and their respective BER for different intensities
of SNR applied to AWGN was attached to each signal and in which we also see that while the intensity
increases the distortion is most noticeable in the watermark as it also increases the power of AWGN. It is noted
that for an AWGN signal strength of 1dB and 5dB the watermark is not distorted, instead to an intensity of
10dB and 15dB are a number of erroneous bits.
Being more resistant to resampling the musical categories of pop and band (see Table 5).
Table 5. Beer for each intensities for awgn
Category SNR
Erroneo
us Bit
Intensitie
s
Bits /
Total Error
for
AWGN
Embedde
d Rate
(dB) Bits (%
BER)
1 0/8 0
5 0/8 0
Pop 10 0/8 0
15 1/8 12.5
1 0/8 0
5 0/8 0
Electronic
10 1/8 12.5
15 2/8 25
1 0/8 0
5 0/8 0
Banda 10 0/8 0
15 1/8 12.5
1 0/8 0
5 0/8 0
Ballad 10 0/8 0
15 2/8 25
1 0/8 0
5 0/8 0
Rock 10 1/8 12.5
15 2/8 25
Compare the original signal with the watermarked signal, and it was subjected to attacks by resampling and
AWGN attacks. The following are conclusions.
IV. Conclusions
A comparison of the signal before being processed and the signal after being processed to verify the visual
difference between them.
We tested the ITU BS.1387-1 standard established inaudibility standards applied in this case, to the
watermark, checking that it is slightly perceptible, if and only if, listening with dedicated attention.
The sampling frequency of the signals before processing was 44.1kHz but to test the robustness of the
method was decreased, and increased the frequency in order to verify what is mentioned in [5] and check if it
was more robust with other rates sampling.
9. Evaluation of Two-Set Algorithm in Embedding a Watermark into an Audio Signal
www.ijres.org 48 | Page
Also verify the behavior of the watermark against the addition of AWGN with different SNR intensities.
It is recommended that the watermark is inserted into the top of each block, because if not done in this
way, the watermark will be easy to detect and extract.
Below are the references consulted, among the items displayed, a book and a standard ITU.
References
[1] M. Hrncar, J. Krajcovic, “Principles of Audio Watermarking”, BytcickaZilina, Slovak Republic, 2005, pp. 1-3.
[2] Nedeljko, C., T. Seppanen, Digital Audio WatermarkingTechniquesand Technologies: Applications andBenchmarks, Information
Science Reference, Hershey NewYork, 2007.
[3] I.K. Yeo, H.J. Kim, “Modified Patchwork Algorithm: A Novel Audio Watermarking Scheme”, IEEE, Transactions on Speech
and Audio Proceesing, Korea, July 2003, pp. 1-6.
[4] D.G. Hong, S.H. Park, and J. Shin, “A Public Key Audio Watermarking using Patchwork Algorithm”, Department of Electronic
Engineering, Dongguk University, Seoul Korea, 2004, pp. 3-4.
[5] H.J. Kim, S. Xiang, and S. Maitra, “Robustness Analysis of Patchwork Watermarking Schemes”, IEEE, Transactions on Speech
and Audio Proceesing, Seoul Korea, 2001, pp. 1-2.
[6] J.E. Gilley, “Bit Error Rate Simulation using Matlab”,Transcrypt International, Inc., August 2003, pp. 1-7.
[7] Recommendation ITU-R BS.1387-1 Method for objective measurements of perceived audio quality, 2001.
AUTHOR PROFILE:
Carlos Aquino Ruiz was born in Mexico on April 2, 1973 is in Communications and Electronics
Engineering from the National Polytechnic Institute IPN, a research professor and areas of
development are applied computing, data networking and security.
Eduardo Pérez Orta. He is a methodological research and has master's. Nowadays it is a research
professor at the School of Mechanical and Electrical Engineering (ESIME) in Mexico. Her areas
of development are assessment and project development, scientific writing and methodology of
science
Celedonio Enrique Aguilar Meza Was Born In Mexico On March 3, 1959 And Holds A Degree In
Communications And Electronics From The National Polytechnic Institute Ipn, Is Currently
Research Professor And Development Areas Are The Processing And Handling, Coding And
Compression, Safety And Efficient Transmission.