Rapid increase in data transmission over internet results in emphasis on information security.
Audio steganography is used for secure transmission of secret data with audio signal as the
carrier. In the proposed method, cover audio file is transformed from space domain to wavelet
domain using lifting scheme, leading to secure data hiding. Text message is encrypted using
dynamic encryption algorithm. Cipher text is then hidden in wavelet coefficients of cover audio
signal. Signal to Noise Ratio (SNR) and Squared Pearson Correlation Coefficient (SPCC)
values are computed to judge the quality of the stego audio signal. Results show that stego
audio signal is perceptually indistinguishable from the cover audio signal. Stego audio signal is
robust even in presence of external noise. Proposed method provides secure and least error
data extraction.
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.
Blind Key Steganography Based on Multilevel Wavelet and CSF irjes
- Steganography is the art and science of invisible communication as it hides the information message
inside cover image In This paper the cover image is decomposed using multilevel wavelet transform and theses
wavelet coefficients are statistically weighted according to their perceptual importance (CSF weights) to identify
the regions of interest for the embedding. The hiding image is encrypted using secret key based on wavelet
coefficients on the last approximation level. Then the encrypted watermark is embedded using CSF weights in
the wavelet domain into the cover image. Experimental results denote the feasibility of the proposed method as
the stego images has high PSNR and subjective quality which declare that the algorithm gains a good
performance in transparency and robustness against noise attacks.
Novel Approach of Implementing Psychoacoustic model for MPEG-1 Audioinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Audio compression has become one of the basic technologies of the multimedia age. The change in the telecommunication infrastructure, in recent years, from circuit switched to packet switched systems has also reflected on the way that speech and audio signals are carried in present systems. In many applications, such as the design of multimedia workstations and high quality audio transmission and storage, the goal is to achieve transparent coding of audio and speech signals at the lowest possible data rates. In other words, bandwidth cost money, therefore, the transmission and storage of information becomes costly. However, if we can use less data, both transmission and storage become cheaper. Further reduction in bit rate is an attractive proposition in applications like remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet.
On the use of voice activity detection in speech emotion recognitionjournalBEEI
Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
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.
Blind Key Steganography Based on Multilevel Wavelet and CSF irjes
- Steganography is the art and science of invisible communication as it hides the information message
inside cover image In This paper the cover image is decomposed using multilevel wavelet transform and theses
wavelet coefficients are statistically weighted according to their perceptual importance (CSF weights) to identify
the regions of interest for the embedding. The hiding image is encrypted using secret key based on wavelet
coefficients on the last approximation level. Then the encrypted watermark is embedded using CSF weights in
the wavelet domain into the cover image. Experimental results denote the feasibility of the proposed method as
the stego images has high PSNR and subjective quality which declare that the algorithm gains a good
performance in transparency and robustness against noise attacks.
Novel Approach of Implementing Psychoacoustic model for MPEG-1 Audioinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Audio compression has become one of the basic technologies of the multimedia age. The change in the telecommunication infrastructure, in recent years, from circuit switched to packet switched systems has also reflected on the way that speech and audio signals are carried in present systems. In many applications, such as the design of multimedia workstations and high quality audio transmission and storage, the goal is to achieve transparent coding of audio and speech signals at the lowest possible data rates. In other words, bandwidth cost money, therefore, the transmission and storage of information becomes costly. However, if we can use less data, both transmission and storage become cheaper. Further reduction in bit rate is an attractive proposition in applications like remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet.
On the use of voice activity detection in speech emotion recognitionjournalBEEI
Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
A novel hash based least significant bit (2 3-3) image steganography in spati...ijsptm
This paper presents a novel 2-3-3 LSB insertion method. The image steganography takes the advantage of human eye limitation. It uses color image as cover media for embedding secret message.The important quality of a steganographic system is to be less distortive while increasing the size of the secret message. In this paper a method is proposed to embed a color secret image into a color cover image. A 2-3-3 LSB insertion method has been used for image steganography. Experimental results show an improvement in the Mean squared error (MSE) and Peak Signal to Noise Ratio (PSNR) values of the proposed technique over the base technique of hash based 3-3-2 LSB insertion.
GENDER RECOGNITION SYSTEM USING SPEECH SIGNALIJCSEIT Journal
In this paper, a system, developed for speech encoding, analysis, synthesis and gender identification is
presented. A typical gender recognition system can be divided into front-end system and back-end system.
The task of the front-end system is to extract the gender related information from a speech signal and
represents it by a set of vectors called feature. Features like power spectrum density, frequency at
maximum power carry speaker information. The feature is extracted using First Fourier Transform (FFT)
algorithm. The task of the back-end system (also called classifier) is to create a gender model to recognize
the gender from his/her speech signal in recognition phase. This paper also presents the digital processing
of a speech signals (pronounced “A” and “B”) which are taken from 10 persons, 5 of them are Male and
the rest of them are Female. Power Spectrum Estimation of the signal is examined .The frequency at
maximum power of the English Phonemes is extracted from the estimated power spectrum. The system uses
threshold technique as identification tool. The recognition accuracy of this system is 80% on average.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Speech compression analysis using matlabeSAT Journals
Abstract The growth of the cellular technology and wireless networks all over the world has increased the demand for digital information by manifold. This massive demand poses difficulties for handling huge amounts of data that need to be stored and transferred. To overcome this problem we can compress the information by removing the redundancies present in it. Redundancies are the major source of generating errors and noisy signals. Coding in MATLAB helps in analyzing compression of speech signals with varying bit rate and remove errors and noisy signals from the speech signals. Speech signal’s bit rate can also be reduced to remove error and noisy signals which is suitable for remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet This paper focuses on speech compression process and its analysis through MATLAB by which processed speech signal can be heard with clarity and in noiseless mode at the receiver end . Keywords: Speech compression, bit rate, filter, MPEG, DCT.
In the recent years, large scale information transfer by remote computing and the development
of massive storage and retrieval systems have witnessed a tremendous growth. To cope up with the
growth in the size of databases, additional storage devices need to be installed and the modems and
multiplexers have to be continuously upgraded in order to permit large amounts of data transfer between
computers and remote terminals. This leads to an increase in the cost as well as equipment. One solution
to these problems is “COMPRESSION” where the database and the transmission sequence can be
encoded efficiently. In this we investigated for optimum wavelet, optimum level, and optimum scaling
factor.
Dual Steganography for Hiding Video in VideoIJTET Journal
Abstract— Dual Steganography is the process of using Steganography combined with Cryptography. Steganography is the process of hiding confidential data’s in the media files such as audio, images, videos, etc. Cryptography is a branch of mathematics concerned with the study of hiding and revealing information and for proving authorship of messages. In this paper, the Dual Steganography concept has been applied to secure the original videos from unauthorized person. The process has been done by embedding the original video inside another video. Both the videos are converted into frames first. After that, the individual frames of original video are sampled with the frames of another video. After completing the sampling process, the output frames are combined to obtain the encrypted video.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Comparative Study of Spatial Domain Image Steganography TechniquesEswar Publications
Steganography is an important area of research in information security. It is the technique of disclosing information into the cover image via. text, video, and image without causing statistically significant modification to the cover image. Secure communication of data through internet has become a main issue due to several passive and active attacks. The purpose of stegnography is to hide the existence of the message so that it becomes difficult for attacker to detect it. Different steganography techniques are implemented to hide the information effectively also researchers contributed various algorithms in each technique to improve the technique’s efficiency. In this paper we do a brief analysis of different spatial domain image stegnography techniques and their comparison. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected.
A ROBUST CHAOTIC AND FAST WALSH TRANSFORM ENCRYPTION FOR GRAY SCALE BIOMEDICA...sipij
In this work, a new scheme of image encryption based on chaos and Fast Walsh Transform (FWT) has been proposed.
We used two chaotic logistic maps and combined chaotic encryption methods to the two-dimensional FWT of images.
The encryption process involves two steps: firstly, chaotic sequences generated by the chaotic logistic maps are used to
permute and mask the intermediate results or array of FWT, the next step consist in changing the chaotic sequences or
the initial conditions of chaotic logistic maps among two intermediate results of the same row or column. Changing the
encryption key several times on the same row or column makes the cipher more robust against any attack. We tested
our algorithms on many biomedical images. We also used images from data bases to compare our algorithm to those
in literature. It comes out from statistical analysis and key sensitivity tests that our proposed image encryption schemeprovides an efficient and secure way for real-time encryption and transmission biomedical images.
DATA HIDING IN AUDIO SIGNALS USING WAVELET TRANSFORM WITH ENHANCED SECURITYcscpconf
Rapid increase in data transmission over internet results in emphasis on information security. Audio steganography is used for secure transmission of secret data with audio signal as the carrier. In the proposed method, cover audio file is transformed from space domain to wavelet domain using lifting scheme, leading to secure data hiding. Text message is encrypted using
dynamic encryption algorithm. Cipher text is then hidden in wavelet coefficients of cover audio signal. Signal to Noise Ratio (SNR) and Squared Pearson Correlation Coefficient (SPCC)
values are computed to judge the quality of the stego audio signal. Results show that stego audio signal is perceptually indistinguishable from the cover audio signal. Stego audio signal is robust even in presence of external noise. Proposed method provides secure and least error data extraction
Audio Steganography Coding Using the Discreet Wavelet TransformsCSCJournals
The performance of audio steganography compression system using discreet wavelet transform (DWT) is investigated. Audio steganography coding is the technology of transforming stego-speech into efficiently encoded version that can be decoded in the receiver side to produce a close representation of the initial signal (non compressed). Experimental results prove the efficiency of the used compression technique since the compressed stego-speech are perceptually intelligible and indistinguishable from the equivalent initial signal, while being able to recover the initial stego-speech with slight degradation in the quality .
A novel hash based least significant bit (2 3-3) image steganography in spati...ijsptm
This paper presents a novel 2-3-3 LSB insertion method. The image steganography takes the advantage of human eye limitation. It uses color image as cover media for embedding secret message.The important quality of a steganographic system is to be less distortive while increasing the size of the secret message. In this paper a method is proposed to embed a color secret image into a color cover image. A 2-3-3 LSB insertion method has been used for image steganography. Experimental results show an improvement in the Mean squared error (MSE) and Peak Signal to Noise Ratio (PSNR) values of the proposed technique over the base technique of hash based 3-3-2 LSB insertion.
GENDER RECOGNITION SYSTEM USING SPEECH SIGNALIJCSEIT Journal
In this paper, a system, developed for speech encoding, analysis, synthesis and gender identification is
presented. A typical gender recognition system can be divided into front-end system and back-end system.
The task of the front-end system is to extract the gender related information from a speech signal and
represents it by a set of vectors called feature. Features like power spectrum density, frequency at
maximum power carry speaker information. The feature is extracted using First Fourier Transform (FFT)
algorithm. The task of the back-end system (also called classifier) is to create a gender model to recognize
the gender from his/her speech signal in recognition phase. This paper also presents the digital processing
of a speech signals (pronounced “A” and “B”) which are taken from 10 persons, 5 of them are Male and
the rest of them are Female. Power Spectrum Estimation of the signal is examined .The frequency at
maximum power of the English Phonemes is extracted from the estimated power spectrum. The system uses
threshold technique as identification tool. The recognition accuracy of this system is 80% on average.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Speech compression analysis using matlabeSAT Journals
Abstract The growth of the cellular technology and wireless networks all over the world has increased the demand for digital information by manifold. This massive demand poses difficulties for handling huge amounts of data that need to be stored and transferred. To overcome this problem we can compress the information by removing the redundancies present in it. Redundancies are the major source of generating errors and noisy signals. Coding in MATLAB helps in analyzing compression of speech signals with varying bit rate and remove errors and noisy signals from the speech signals. Speech signal’s bit rate can also be reduced to remove error and noisy signals which is suitable for remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet This paper focuses on speech compression process and its analysis through MATLAB by which processed speech signal can be heard with clarity and in noiseless mode at the receiver end . Keywords: Speech compression, bit rate, filter, MPEG, DCT.
In the recent years, large scale information transfer by remote computing and the development
of massive storage and retrieval systems have witnessed a tremendous growth. To cope up with the
growth in the size of databases, additional storage devices need to be installed and the modems and
multiplexers have to be continuously upgraded in order to permit large amounts of data transfer between
computers and remote terminals. This leads to an increase in the cost as well as equipment. One solution
to these problems is “COMPRESSION” where the database and the transmission sequence can be
encoded efficiently. In this we investigated for optimum wavelet, optimum level, and optimum scaling
factor.
Dual Steganography for Hiding Video in VideoIJTET Journal
Abstract— Dual Steganography is the process of using Steganography combined with Cryptography. Steganography is the process of hiding confidential data’s in the media files such as audio, images, videos, etc. Cryptography is a branch of mathematics concerned with the study of hiding and revealing information and for proving authorship of messages. In this paper, the Dual Steganography concept has been applied to secure the original videos from unauthorized person. The process has been done by embedding the original video inside another video. Both the videos are converted into frames first. After that, the individual frames of original video are sampled with the frames of another video. After completing the sampling process, the output frames are combined to obtain the encrypted video.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Comparative Study of Spatial Domain Image Steganography TechniquesEswar Publications
Steganography is an important area of research in information security. It is the technique of disclosing information into the cover image via. text, video, and image without causing statistically significant modification to the cover image. Secure communication of data through internet has become a main issue due to several passive and active attacks. The purpose of stegnography is to hide the existence of the message so that it becomes difficult for attacker to detect it. Different steganography techniques are implemented to hide the information effectively also researchers contributed various algorithms in each technique to improve the technique’s efficiency. In this paper we do a brief analysis of different spatial domain image stegnography techniques and their comparison. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected.
A ROBUST CHAOTIC AND FAST WALSH TRANSFORM ENCRYPTION FOR GRAY SCALE BIOMEDICA...sipij
In this work, a new scheme of image encryption based on chaos and Fast Walsh Transform (FWT) has been proposed.
We used two chaotic logistic maps and combined chaotic encryption methods to the two-dimensional FWT of images.
The encryption process involves two steps: firstly, chaotic sequences generated by the chaotic logistic maps are used to
permute and mask the intermediate results or array of FWT, the next step consist in changing the chaotic sequences or
the initial conditions of chaotic logistic maps among two intermediate results of the same row or column. Changing the
encryption key several times on the same row or column makes the cipher more robust against any attack. We tested
our algorithms on many biomedical images. We also used images from data bases to compare our algorithm to those
in literature. It comes out from statistical analysis and key sensitivity tests that our proposed image encryption schemeprovides an efficient and secure way for real-time encryption and transmission biomedical images.
DATA HIDING IN AUDIO SIGNALS USING WAVELET TRANSFORM WITH ENHANCED SECURITYcscpconf
Rapid increase in data transmission over internet results in emphasis on information security. Audio steganography is used for secure transmission of secret data with audio signal as the carrier. In the proposed method, cover audio file is transformed from space domain to wavelet domain using lifting scheme, leading to secure data hiding. Text message is encrypted using
dynamic encryption algorithm. Cipher text is then hidden in wavelet coefficients of cover audio signal. Signal to Noise Ratio (SNR) and Squared Pearson Correlation Coefficient (SPCC)
values are computed to judge the quality of the stego audio signal. Results show that stego audio signal is perceptually indistinguishable from the cover audio signal. Stego audio signal is robust even in presence of external noise. Proposed method provides secure and least error data extraction
Audio Steganography Coding Using the Discreet Wavelet TransformsCSCJournals
The performance of audio steganography compression system using discreet wavelet transform (DWT) is investigated. Audio steganography coding is the technology of transforming stego-speech into efficiently encoded version that can be decoded in the receiver side to produce a close representation of the initial signal (non compressed). Experimental results prove the efficiency of the used compression technique since the compressed stego-speech are perceptually intelligible and indistinguishable from the equivalent initial signal, while being able to recover the initial stego-speech with slight degradation in the quality .
Steganography analysis techniques applied to audio and image filesjournalBEEI
The present work carries out a descriptive analysis of the main steganography techniques used in specific digital media such as audio and image files. For this purpose, a literary review of the domains, methods, and techniques as part of this set was carried out and their functioning, qualities, and weaknesses are identified. Hence, it is concluded that there is a wide relationship between audio and image steganography techniques in their implementation form. Nevertheless, it is determined that LSB is one of the weakest techniques, but the safest and the most robust technique within each type of the presented medium.
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
This paper presents a general overview of the steganography. Steganography is the art of hiding the very presence of
communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of
steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a
great deal of attention both from law enforcement and the media. In this paper review the what data types are supported, what methods
and information security professionals indetecting the use of steganography, after detection has occurred, can the embedded message
be reliably extracted, can the embedded data be separated from the carrier revealing the original file, and finally, what are some
methods to defeat the use of steganography even if it cannot be reliably detected.
DWT-SMM-based audio steganography with RSA encryption and compressive samplingTELKOMNIKA JOURNAL
Problems related to confidentiality in information exchange are very important in the digital computer era. Audio steganography is a form of a solution that infuses information into digital audio, and utilizes the limitations of the human hearing system in understanding and detecting sound waves. The steganography system applies compressive sampling (CS) to the process of acquisition and compression of bits in binary images. Rivest, Shamir, and Adleman (RSA) algorithms are used as a system for securing binary image information by generating encryption and decryption key pairs before the process is embedded. The insertion method uses statistical mean manipulation (SMM) in the wavelet domain and low frequency sub-band by dividing the audio frequency sub-band using discrete wavelet transform (DWT) first. The optimal results by using our system are the signal-to-noise ratio (SNR) above 45 decibel (dB) and 5.3833 bit per second (bps) of capacity also our system has resistant to attack filtering, noise, resampling and compression attacks.
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.
Drubbing an Audio Messages inside a Digital Image Using (ELSB) MethodIOSRJECE
It is mainly focused today to transfer the messages secretly between two communication parties. The message from the sender to receiver should be kept secret so that the information should not known by anyone. Secret is the important thing today. The technique that is used for secure communication is called as steganography and it means that to hide secret information into innocent data. Digital images are ideal for hiding secret information. An image containing a secret message is called a cover image. In this paper will discuss about secret transformation of audio messages. The audio messages are hidden inside a cover image so no one can hack the audio but the audio should be encrypted before hidden inside the image
Using SVD and DWT Based Steganography to Enhance the Security of Watermarked ...TELKOMNIKA JOURNAL
Watermarking is the process of embedding information into a carrier file for the protection of ownership/copyright of digital media, whilst steganography is the art of hiding information. This paper presents, a hybrid steganographic watermarking algorithm based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) transforms in order to enhance the security of digital fingerprint images. A facial watermark is embedded into fingerprint image using a method of singular value replacement. First, the DWT is used to decompose the fingerprint image from the spatial domain to the frequency domain and then the facial watermark is embedded in singular values (SV’s) obtained by application of SVD. In addition, the original fingerprint image is not required to extract the watermark. Experimental results provided demonstrate the methods robustness to image degradation and common signal processing attacks, such as histogram and filtering, noise addition, JPEG and JPEG2000 compression with various levels of quality.
Audio Steganography Using Discrete Wavelet Transformation (DWT) & Discrete Co...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Adaptive Steganography Based Enhanced Cipher Hiding Technique for Secure Data...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Similar to DATA HIDING IN AUDIO SIGNALS USING WAVELET TRANSFORM WITH ENHANCED SECURITY (20)
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. 138
Computer Science & Information Technology (CS & IT)
knowledge of the existence of the communication. Steganography can be a solution which makes
it possible to send news and information without being censored and without the fear of the
messages being intercepted and traced back. Steganography becomes more important as more
people join cyberspace revolution [2]. In audio steganography, the weak point of Human
Auditory System (HAS) is used to hide information in the audio. Because the human auditory
system has more accurate than Human Visual System (HVS), audio steganography is more
challenging than image steganography. Audio file used to hide the secret message is called as
cover audio and once secret data is embedded into cover audio, resultant audio is called as stego
audio. The three important parameters in designing steganography method are perceptual
transparency, hiding capacity and robustness. The hidden information is imperceptible if a
listener is unable to single out between the cover- and the stego-audio signal. Hiding capacity
refers to the amount of obscured data (in bits) within a cover audio signal. The robustness criteria
are assessed through the survival of concealed data against noise and manipulations of the audio
signal [3]. Three prominent data embedding approaches have been investigated, namely hiding in
temporal domain, in Transform domains and in coded domain. Out of these wavelet transform
provides more security and robustness than the other approaches.
1.1 Wavelet transformation of audio signal
The wavelet transform (WT) has gained widespread acceptance in signal processing and image
compression. Wavelet transform is the breaking up of a signal into shifted and scaled versions of
the original (or mother) wavelet [4]. A wavelet is a waveform of effectively limited duration that
has an average value of zero. For signals; identity of the signal is given by the low-frequency
component. The high-frequency content only imparts savour or nuance. In human voice, if highfrequency components are removed, the voice sounds different, but still it can be understood. If
low frequency components are removed, signal sounds gabble. On applying wavelet
transformations on audio signal, approximation and detail components of audio can be obtained.
The approximations are low-frequency components of the signal and details are high-frequency
components. The first level detail coefficients have less importance in comparison with detail
coefficients of next levels and approximation coefficients because of their low energy level.
Figure 1 shows the decomposition of audio signal on wavelet transform.
Figure 1. One stage signal decomposition
1.2 Lifting Wavelet Transform (LWT)
The lifting scheme is a technique for both designing wavelets and performing the Discrete
Wavelet Transform (DWT) [5].The problem with DWT is that when applying it on an integer
signal, the resulted coefficients are not integers. If the algorithm needs to access the binary value
of the resultant coefficients, then conversion of coefficients from floating to binary will require to
scale and then convert them to a binary. To solve this problem, lifting scheme can be used to
produce integer to integer wavelets. In this, the resultant coefficients are integers for all integer
3. Computer Science & Information Technology (CS & IT)
139
signals. This eliminates the need for scaling the coefficients and converting them to binary
representation. The main sources of errors arise during this conversion such as rounding errors
and out of range errors, do not occur.
Section 2 will brief on some of related works which has been done on audio steganography.
Section 3 will explain the methodology. Section 4 contains experimental results and analysis. The
Final section will brief on conclusion.
2. LITERATURE SURVEY
In recent years, several researchers have concentrated on developing algorithms for hiding data in
an audio signal. Jisna Antony et al [6] discuss about different audio steganographic techniques
available in different domain. Lots of work is done in all domains. Baritha and Venkataramani [7]
propose a new dictionary based text compression technique. Dictionary based compression bits
are hidden into the LSB bit of audio signals. In this secret text is hidden using an identifier.
Identifier along with length and width are hidden inside audio. Identifier indicates whether there
is secret text hidden or not. This paper is implemented in temporal domain. Ahmad Delforouzi
and Mohammad Pooyan [8] proposes an algorithm which embeds secret data in temporal domain.
In this algorithm first embedding threshold in the time domain is estimated. Then this threshold is
used for data concealment in the time domain. Drawback of audio steganography in temporal
domain is even though it is easy to hide data; it lacks security as well as has less hiding capacity
compared to hiding in wavelet domain. Dora and Juan [9] proposes a new scheme of data hiding
which takes advantage of the masking property of the Human Auditory System (HAS) to hide a
secret (speech) signal into a host (speech) signal. In embedding process, wavelet coefficients of
the secret signal are sorted and embedded in the wavelet coefficients of the host signal. And their
original positions are used as key. Delay is inserted in each cycle to achieve synchronization. This
approach consumes more time; retrieved secret signal is not same as the original because there is
error in reconstruction of host signal. Also as there is need to store the positions of frames in
stego signal, it reduces the hiding capacity of the host signal. Yongfeng Huang et al [10],
proposed an algorithm which performs data embedding while pitch period prediction is
conducted. Embedding the secret data is done during low bit-rate speech encoding. Drawback of
this technique is that stego audio has been detected in steganalysis. Parul Shah et.al, [11]
developed an algorithm where modification of host audio is done by imposing a constraint which
forces the modified value to be in the same range as its neighbourhood. In this paper, host signal
is decomposed using wavelet packet up to third level, then selected band coefficients are sampled
and then converted to 2D. This 2D matrix is then divided to 2X2 non overlapping blocks used to
embed covert data using pseudorandom sequence. Secret data is embedded into host based on
trend mapping. Sajad Shirali-Shahreza and M.T. Manzuri-Shalmani [12] developed an audio
steganography algorithm to hide text which uses lifting scheme to create perfect reconstruction.
In this secret data is stored based on the details coefficient value. To calculate the number of bits
to hold data in a coefficient with value ‘c’, the biggest power of 2 named ‘p’ which is smaller
than ’c’ i.e., 2p≤ c <2p+1 is found out. The number of bits used to hide in this coefficient is p –
OBH where Original Bit to Hold (OBH) is a constant which shows how many bits of the original
signal is kept unchanged so that stego audio is imperceptible and how many bits of the signal are
replaced with the data. Ahmad Delforouzi [13] describes an algorithm where LWT is applied on
host audio signal. Host audio signal is decomposed to fifth level and sub bands are used to hide
the secret data using the threshold calculated. Drawback of this algorithm is that threshold value
calculation.
4. 140
Computer Science & Information Technology (CS & IT)
3. PROPOSED METHOD
This section discusses the algorithm used to hide encrypted text in cover audio signal. Algorithm
has two phases – embedding and extraction. In embedding phase, encrypted text is hidden inside
the cover audio signal. It should be made sure that there should not be any distortion in the cover
audio by hiding the secret data. In extraction phase, the secret text is retrieved from the stegoaudio. In this algorithm, audio samples are transformed into wavelet domain. Secret data here is
text, which is encrypted using dynamic encryption algorithm. These transformed values of text
are then hidden in LSB’s of detail coefficients.
3.1 Embedding Phase
Step 1: Audio Processing
Read the cover audio file. Audio samples are stored in a vector and are signed floating point
values. When an audio signal is transformed to another domain, then changed back to time
domain, the resulting signal is not necessarily integer. In order to get integer coefficients from
audio samples, audio samples must be converted to integers. This conversion is performed here.
Step 2: Apply LWT based on lifting scheme
This algorithm uses integer to integer transformation which is implemented using lifting wavelet
transformation (LWT). LWT uses Lifting Scheme (LS). In LS, among the various wavelets
available, appropriate wavelet is chosen. As integer coefficients are required, ‘int2int’
transformation has to be specified. Based on the LS, apply the LWT to cover audio to get detail
and approximation coefficients, CD and CA respectively. Convert CD to binary.
Step 3: Calculate number of bits to be replaced.
The number of bits used to hold data is calculated using the logic explained by Sajad ShiraliShahreza and M.T. Manzuri-Shalmani [12]. This algorithm chooses dynamic approach to find the
bits to hold the secret text. Detail coefficients are selected to hold the secret text. Number of bits
of CD to be replaced (NBR) is based on the fact that if the coefficient value is more, then
changing more bits will not cause major difference in the signal. So, more secret data bits are
hidden in bigger coefficients and fewer in smaller coefficients.
Step 4: Read text file and encrypt it.
Read the text file. Find the size of the text to be hidden. The text is encrypted by subtracting
ASCII value of each character by message size. Cipher text is then converted to binary string.
Reason behind implementing simple encryption technique using message size is that there is no
need to hide the encryption key in cover audio. This allows more data to be hidden and also
provides security without reducing the hiding capacity.
Step 5: Embed the encrypted text
This step is sub divided into two parts: Hiding the size of the text and hiding the actual text.
“Text” is also referred as “message” in rest of the paper.
5. Computer Science & Information Technology (CS & IT)
•
141
Hide message size
It is necessary to embed the secret message size into the cover audio because during
extraction of text from stego-audio, receiver should know how many bits have to be extracted
from stego-audio. Also, receiver has to decrypt the secret text based on message size. First 16
replaceable LSB’s are reserved to store the message size, based on NBR calculated for each
CD value. Message size bits, starting from MSB, are stored in LSB’s of CD’s.
•
Hide the actual message
Remaining replaceable bits of each CD are used to store the encrypted secret message.
Message bits starting from MSB, are stored in LSB’s of each CD.
Step 6: Reconstruction of stego-audio signal.
After embedding the secret message into CD; using CA and modified CD, stego- audio signal is
reconstructed by applying inverse LWT. This stego-audio sounds same as the cover audio.
Figure 2 shows the embedding process.
3.2 Extraction Phase
Step 1: Audio Processing
Read the stego audio file. Then convert the audio samples into integers. This step is same as step
1 in the embedding phase.
Step 2: Apply LWT based on lifting scheme
Select the same lifting scheme which is used in the embedding phase. Based on this LS, apply the
LWT to cover audio to get detail and approximation coefficients, CD and CA respectively.
Convert CD to binary.
Step 3: Calculate number of bits to be replaced
This is exactly same as step 3 in the embedding phase. Use same OBH value that is used in the
embedding phase.
Step 4: Extract the hidden message
This step is sub divided into two parts: retrieve the size of the message and retrieve the message.
• Retrieve message size
Based on NBR calculated for each CD in step 3, 16 bits are retrieved from LSB’s of CD’s to
obtain the message size.
• Retrieve the message
Encrypted message bits starting from MSB, are retrieved from LSB’s of remaining CD’s
using the message size and store it in a buffer.
Step 5: Decryption of message and writing it into a file
After retrieving, the encrypted secret message bits are converted to decimal. Resultant message
bits are then decrypted using extracted message size and converted to character. This is again
written to an output text file. Figure 3 depicts the extraction phase.
6. 142
Computer Science & Information Technology (CS & IT)
4. EXPERIMENTAL RESULTS
This section focuses on the experimental results. Results for different audio files with different
amount of data hidden are shown. Quality of the stego-audio is analyzed using MSE and SNR.
MSE serves as an important parameter in gauging the performance of the steganographic system.
Suppose that x = {xi | i = 1, 2. . . N} and y = {yi | i = 1, 2. . . N} are two finite-length, discrete
signals, for e.g., visual images or audio signals. Then MSE between the signals is given by
equation (4).
where,
N is the number of signal samples.
xi is the value of the ith sample in x.
yi is the value of the ith sample in y.
SNR is a term that refers to the measurement of the level of an audio signal as compared to the
level of noise that is present in that signal. It is expressed in decibels (dB). A larger SNR value
indicates a better quality. It is given by equation (5).
7. Computer Science & Information Technology (CS & IT)
143
Recommended SNR for audio signal is above 30dB.
Another metric based on correlation of samples is Squared Pearson Correlation Coefficient
(SPCC). The higher the SPCC, the better is the quality of the output signal. Its range is between 0
and 1. It is given by equation (6).
where x, y, and are the cover signal, stego signal, average of the cover signal and average of
the stego signal, respectively.
The algorithm is implemented in MATLAB 11 on an Intel core 2 Duo CPU at 2.00 GHz with
2.00 GB RAM. Four audio files Two.wav with 276347 samples, Woody2.wav with 37620
samples, b.wav with 77175 samples and 1.wav with 69860 samples are considered as cover audio
signals. Four text files Test1.txt consisting of 22 characters, Test2.txt consisting of 2739
characters, Test3.txt consisting of 4328 characters, Test4.txt consisting of 5 characters and
Test5.txt with 5737 characters are considered as secret messages to test the algorithm. Table 1
shows MSE, SNR and SPCC values of various audio signals when different amount of secret
message is hidden with OBH equal to 1. It is observed from Table 1 that SNR decreases and MSE
increases, as the hiding capacity is increased. Maximum hiding capacity of any cover audio
signal depends upon the sample values. Wavelet used is “db2”. White Gaussian noise with
different SNR values is added to stego audio signal. Secret data is able to be retrieved without
any errors. This is used to check the robustness of the algorithm. Experiment is conducted with
other wavelets as well; there is no significance change in the results.
TABLE 1. MSE, SNR and SPCC values for different hiding capacities
Cover Audio
Text file
Number
characters
Two.wav
Test1.txt
Woody2.wav
of
MSE
SNR(dB)
SPCC
22
0.0069
63.79
0.9432
Test1.txt
22
0.0083
63.36
0.9385
Two.wav
Test2.txt
2739
0.9975
42.20
0.9058
Woody2.wav
Test2.txt
2739
2.51
38.57
0.9001
Two.wav
Test3.txt
4328
1.5432
40.30
0.9039
Woody2.wav
Test5.txt
5737
5.2778
35.34
0.8982
b.wav
Test4.txt
5
0.0057
67.56
0.9835
b.wav
Test1.txt
22
0.0086
66.79
0.9832
1.wav
Test4.txt
5
0.0016
73.35
0.9895
1.wav
Test1.wav
22
0.0086
65.79
0.9751
Experiment is also conducted with different OBH values.
tabulated in Table 2.
Results of embedding Test1.txt are
8. 144
Computer Science & Information Technology (CS & IT)
TABLE 2. MSE and SNR(dB) values with different OBH
Audio
OBH=2
OBH=4
MSE
SNR
SPCC
MSE
SNR
SPCC
Two.wav
0.0035
66.71
0.9851
0.0021
67.90
0.9897
Woody2.wav
0.0056
65.08
0.9765
0.0043
66.25
0.9855
From Table 2, it is clear that if more number of bits are replaced, hiding capacity increases but
MSE increases and also audio quality is degraded.
Figures 4, 6 and 8 shows the cover, stego and stego with noise audio with encrypted text being
hidden in woody2.wav, respectively. It can be observed that significant changes are not
perceptible. Figure 5 shows the secret message to be hidden. It shows the original secret message
and encrypted message. Figure 7 shows the output of extraction phase, it shows the received
encrypted message and decrypted message from stego-audio.
Figure 4. cover audio – woody2.wav
Figure 5. Test1.txt embedded in Two.wav
9. Computer Science & Information Technology (CS & IT)
145
Figure 6 stego-audio
Figure 7. Retrieved secret text
Figure 8. Stego-audio with white Gaussian noise
Subjective tests for audio quality evaluation are also performed. Five listeners were presented
with a set of audio clips containing six songs, two original and two stego and two stego audio
added with white Gaussian noise, in a random order. For most of the cases, listener could not
differentiate between the different between original and stego audio, i.e., noise was inaudible.
These results show the proposed method does not degrade the audio quality for almost all the
cases.
10. 146
Computer Science & Information Technology (CS & IT)
5. CONCLUSION
Objective of the paper is to hide encrypted text in cover audio using lifting wavelet transform.
Based on the values of coefficients, number of bits used to hold secret data is chosen. In the
proposed method, text is encrypted based on the message size and then hidden in cover audio.
Results are computed and observed. This algorithm yields zero error extraction, good SNR and
SPCC. Similar technique is used by Sajad Shirali-Shahreza and M.T. Manzuri-Shalmani [12]
without encryption. There SPCC is not calculated, which is a good metric to test the audio quality
based correlation. In the proposed method approximately same values of SNR and MSE are
obtained as in [12] even with encryption and noise added. As the audio samples for even 30 secs
audio file is in lakhs, processing it and hiding text and extracting it takes lot of time. This
drawback can be eliminated by implementing in parallel using GPU’s.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
K. Ramani et al, “Steganography using BPCS to the integer wavelet transformed image”, IJCSNS
International Journal of Computer Science and Network Security, VOL.7 No.7, 2007, pp. 293- 302
Fatiha Djebbar et al,” Comparative study of digital audio steganography techniques”, EURASIP
Journal on Audio, Speech, and Music Processing 2012,pp no. 1192-1203
Abbas Cheddad, ”Digital image steganography: Survey and analysis of current methods”,Signal
Processing,Vol 90,Issue 3,March 2010,pp. 727-752
Michael Weeks, ”Digital Signal Processing Using MATLAB and Wavelets”, Pearson publications,
ISBN – 81-297-0272-X.
Elham Ghasemi, Jamshid Shanbehzadeh and Bahram ZahirAzami, "A Steganographic method based
on Integer Wavelet Transform and Genetic Algorithm", IEEE conference 2011.
Jisna Antony and Sobin C,” Audio Steganography in Wavelet Domain – A Survey”, International
Journal of Computer Applications, Volume 52, No.13, 2012, pp. 33-37
M.Baritha Begum and Y.Venkataramani, ”LSB Based Audio Steganography Based on Text
Compression”, International Conference on Communication Technology and System Design,
2011,pp. 703-710
Ahmad Delforouzi and Mohammad Pooyan, “Adaptive and Efficient Audio Data Hiding Method in
Temporal Domain”, IEEE ICICS, 2009.
Dora M. Ballesteros L and Juan M. Moreno A,” Real-time, speech-in-speech hiding scheme based on
least significant bit substitution and adaptive key”, Computers and Electrical Engineering Vol 39,
Elsevier, 2013,pp. 1192-1203
Yongfeng Huang, Chenghao Liu and Shanyu Tang,” Steganography Integration into a Low-Bit Rate
Speech Codec”, IEEE transactions on information forensics and security, vol. 7, no. 6, 2012
P. Shah, P. Choudhari, and S. Sivaraman, “Adaptive wavelet packet based audio steganography using
data history", IEEE Region 10 and the Third international Conference on Industrial and Information
Systems, ICIIS, IEEE, 2008.
S. Shahreza and M. Shalmani, “High capacity error free wavelet domain speech steganography",
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2008.
Mohammad Pooyan, Ahmad Delforouzi, ”Adaptive Digital Audio Steganography based on Integer
Wavelet Transform”, Circuits, Systems, Signal Process, 2008.