This document discusses hiding text in audio files using least significant bit (LSB) based steganography. It proposes a new algorithm that embeds text by converting characters to 5-bit codes based on redundancies in binary representations of numbers and letters. The text is then hidden in an audio file by replacing the LSB of audio samples with the 5-bit codes. The performance is evaluated using mean opinion scores from human listeners and by comparing signal-to-noise ratios with other algorithms. Tests were conducted by hiding text in audio files with different sample sizes, rates, bitrates, and file sizes and asking listeners to determine if text was hidden and rate the perceived quality.
Data Security Using Audio SteganographyRajan Yadav
Steganography is the art and science of writing hidden messages in such a way that no
one, apart from the sender and intended recipient, suspects the existence of the message,
a form of security through obscurity. Steganography works by replacing bits of useless or
unused data in regular computer files (such as graphics, sound, text, HTML, or even
floppy disks ) with bits of different, invisible information. This hidden information can
be plain text, cipher text, or even images.
In a computer-based audio Steganography system, secret messages are embedded in
digital sound. The secret message is embedded by slightly altering the binary sequence of
a sound file. Existing audio Steganography software can embed messages in WAV, AU,
and even MP3 sound files. Embedding secret messages in digital sound is usually a more
difficult process than embedding messages in other media, such as digital images. These
methods range from rather simple algorithms that insert information in the form of signal
noise to more powerful methods that exploit sophisticated signal processing techniques
to hide information.
This document summarizes three algorithms for audio steganography. Algorithm 1 embeds watermark bits randomly in audio samples. Algorithm 2 embeds bits in the first k elements of each audio segment. Algorithm 3 embeds bits in the first k segments. Algorithm 3 is most imperceptible but least robust, while Algorithm 1 is most robust but least imperceptible. The embedding position affects imperceptibility and robustness, which are inversely related. The document evaluates the algorithms' performance and concludes that changing the embedding position impacts an audio signal's imperceptibility and robustness.
This document is a synopsis submitted for a degree in bachelor of technology. It describes a project on audio steganography, where a secret message is hidden in a digital audio file. The synopsis includes an introduction describing the objective, benefits and scope of the project. It also includes sections on the encoding and decoding algorithms, flow charts, use case and data flow diagrams, and references.
This document discusses audio steganography. It defines steganography as hiding a message such that no one apart from the sender and recipient knows about the message. It describes different methods of hiding information in audio files, including least significant bit and discrete wavelet transform methods. It outlines the advantages of audio steganography such as its ability to conceal more information and flexibility. It also includes project plans, risk analysis, cost analysis, and UML diagrams for an audio steganography software project.
This document describes a project that aims to improve mobile banking security using steganography. It discusses the existing mobile banking system and its disadvantages like time constraints, high communication costs, and lack of security. The proposed system would use steganography to hide banking transaction information in images, providing higher security. It presents the system architecture, use case diagram, sequence diagrams, activity diagram, and class diagram to analyze and design the secure mobile banking system using steganography. In conclusion, the project presents a method to increase security of user information by hiding it in images using steganography instead of direct transmission.
Nowadays, several methods are used for communicating secret messages for defense purposes or in order to ensure the privacy of communication between two parties. So we go for hiding information in ways that prevent its detection.
The document discusses steganography, which is the art of hiding information within other files like images. It explains how early Greeks used steganography by engraving messages in wood and covering it with wax. Modern steganography uses computers to hide information by changing the least significant bit of image file bytes, which is imperceptible to the human eye. The document also provides an overview of a proposed steganography application that allows users to hide text within an image file and later extract the hidden text.
This document describes a mini project on steganography submitted by four students to fulfill the requirements of a computer science degree. It includes a cover page, certificates from advisors and department heads, acknowledgements, a declaration page, and a table of contents. The project involves developing an application for hiding secret messages in digital images using the least significant bit steganography technique. It covers encoding a message into an image, decrypting the message from the stego-image, and a user manual for the application. The objective is to explore data hiding and extraction techniques for secure communication.
Data Security Using Audio SteganographyRajan Yadav
Steganography is the art and science of writing hidden messages in such a way that no
one, apart from the sender and intended recipient, suspects the existence of the message,
a form of security through obscurity. Steganography works by replacing bits of useless or
unused data in regular computer files (such as graphics, sound, text, HTML, or even
floppy disks ) with bits of different, invisible information. This hidden information can
be plain text, cipher text, or even images.
In a computer-based audio Steganography system, secret messages are embedded in
digital sound. The secret message is embedded by slightly altering the binary sequence of
a sound file. Existing audio Steganography software can embed messages in WAV, AU,
and even MP3 sound files. Embedding secret messages in digital sound is usually a more
difficult process than embedding messages in other media, such as digital images. These
methods range from rather simple algorithms that insert information in the form of signal
noise to more powerful methods that exploit sophisticated signal processing techniques
to hide information.
This document summarizes three algorithms for audio steganography. Algorithm 1 embeds watermark bits randomly in audio samples. Algorithm 2 embeds bits in the first k elements of each audio segment. Algorithm 3 embeds bits in the first k segments. Algorithm 3 is most imperceptible but least robust, while Algorithm 1 is most robust but least imperceptible. The embedding position affects imperceptibility and robustness, which are inversely related. The document evaluates the algorithms' performance and concludes that changing the embedding position impacts an audio signal's imperceptibility and robustness.
This document is a synopsis submitted for a degree in bachelor of technology. It describes a project on audio steganography, where a secret message is hidden in a digital audio file. The synopsis includes an introduction describing the objective, benefits and scope of the project. It also includes sections on the encoding and decoding algorithms, flow charts, use case and data flow diagrams, and references.
This document discusses audio steganography. It defines steganography as hiding a message such that no one apart from the sender and recipient knows about the message. It describes different methods of hiding information in audio files, including least significant bit and discrete wavelet transform methods. It outlines the advantages of audio steganography such as its ability to conceal more information and flexibility. It also includes project plans, risk analysis, cost analysis, and UML diagrams for an audio steganography software project.
This document describes a project that aims to improve mobile banking security using steganography. It discusses the existing mobile banking system and its disadvantages like time constraints, high communication costs, and lack of security. The proposed system would use steganography to hide banking transaction information in images, providing higher security. It presents the system architecture, use case diagram, sequence diagrams, activity diagram, and class diagram to analyze and design the secure mobile banking system using steganography. In conclusion, the project presents a method to increase security of user information by hiding it in images using steganography instead of direct transmission.
Nowadays, several methods are used for communicating secret messages for defense purposes or in order to ensure the privacy of communication between two parties. So we go for hiding information in ways that prevent its detection.
The document discusses steganography, which is the art of hiding information within other files like images. It explains how early Greeks used steganography by engraving messages in wood and covering it with wax. Modern steganography uses computers to hide information by changing the least significant bit of image file bytes, which is imperceptible to the human eye. The document also provides an overview of a proposed steganography application that allows users to hide text within an image file and later extract the hidden text.
This document describes a mini project on steganography submitted by four students to fulfill the requirements of a computer science degree. It includes a cover page, certificates from advisors and department heads, acknowledgements, a declaration page, and a table of contents. The project involves developing an application for hiding secret messages in digital images using the least significant bit steganography technique. It covers encoding a message into an image, decrypting the message from the stego-image, and a user manual for the application. The objective is to explore data hiding and extraction techniques for secure communication.
The document provides an overview of steganography, including its definition, history, techniques, applications, and future scope. It discusses different types of steganography such as text, image, and audio steganography. For image steganography, it describes techniques such as LSB insertion and compares image and transform domain methods. It also provides examples of steganography tools and their usage for confidential communication and data protection.
Steganography is a technique for hiding secret messages within other non-secret files like images, audio, or video. It works by embedding messages into the least significant bits of pixel data in an image or by masking digital signatures in portions of audio or video files. The document discusses steganography tools and algorithms for hiding data in images using techniques like LSB insertion and encryption algorithms like DES. It also covers data compression methods like Lempel-Ziv-Welch used for reducing file sizes.
This document discusses a steganography project developed using C# that allows hiding information within image files. It provides an introduction to steganography and its importance. It then describes the software tools used, including C# and various namespaces. It outlines the project scope, requirements, and flowchart. It also includes code analysis and explanations of the encryption and decryption processes used to hide a file within an image without detection. Finally, it discusses future applications and concludes with thanking those who helped with the project.
This document is a project report for a BTech student named Rohit Jaiswal on image and audio steganography. It discusses hiding text messages in digital images and audio files. The report includes an introduction to steganography and cryptography, an analysis of digital images and audio, the design of the steganography system including algorithms, and implementations in C# and Flash. It also covers inputs, outputs, and conclusions with references and source code in the appendix. The supervisor of the project is Mr. Rajeev Srivastava of the Computer Science department at Banaras Hindu University.
The document describes a technical seminar report on an adaptive LSB-OPAP based secret data hiding technique. It was submitted by Tejas.S, an undergraduate student of electronics and communication engineering at Amruta Institute of Engineering and Management Sciences, in partial fulfillment of the requirements for their bachelor's degree. The report discusses a new technique for embedding secret data into color or grayscale images using LSB substitution in an adaptive manner based on pixel intensity ranges. It aims to enhance embedding capacity and imperceptibility compared to traditional LSB techniques.
Steganography is a technique for hiding secret information within ordinary digital files so that the very existence of the hidden information is concealed. It works by replacing bits of redundant data within image, audio, or video files with bits of the secret message. This allows secure communication of hidden information in a way that avoids detection. The document discusses the history and benefits of steganography, providing examples of its use throughout history for covert communication. It also introduces some key concepts and terminology used in modern steganography.
While transferring a file from one point to another through Intranet and Internet we need more file secure concepts. Ordinary, file Encryption-Decryption Concepts, which are readily available in java examples are easily captured by middle way itself. So we need more security combination. This project helps to send a file from one place to another in a secured manner. Firstly the target file is encrypted and it is embedded into an audio or video or any media file. The resultant file will be protected by a password. This resultant media file is not changed in its original format and it can be run in the player, we can’t find any encrypted data inside it. This format will be sent through net. In the destination point it will be retrieved only by our software and giving the relevant password. So it is highly secured.
This document is a self-study seminar report on steganography presented by Abhishek Singh. It includes an introduction to steganography, its importance, and a literature review on different steganographic techniques. The introduction defines steganography as hidden writing and discusses why it is important for secret communications. The literature review covers steganography methods in text, audio, images, and networks. It also discusses steganalysis, applications of steganography, and how it compares to cryptography.
The document discusses data hiding in audio signals using watermarking techniques. It provides an introduction to watermarking and its applications. It describes the basic watermarking system as similar to a communication system. It also discusses speech processing, wavelet analysis, hiding techniques, advantages and disadvantages of audio watermarking. The document presents results from watermarking an audio signal and inserting a speech signal. It concludes that audio watermarking can be used for information tracing, tamper detection and other purposes beyond covert communication.
Steganography is the art of hiding secret messages within other harmless-looking files like images, audio, or video. It embeds data in the least significant bits of pixels in images or within the phase components of audio files, making the hidden messages undetectable without access to the steganographic algorithm and key. Modern steganography techniques include least significant bit insertion and masking/filtering data in image compression algorithms. Steganography differs from cryptography by concealing the existence of secret communication, rather than just scrambling its contents.
This document provides an overview of steganography. It defines steganography as hiding secret messages within other harmless messages to avoid detection by unauthorized parties. Various steganographic techniques are described, including hiding messages in digital files like images, audio, text and network protocols. Detection of steganography (steganalysis) and applications like digital watermarking and tamper proofing are also discussed. The document concludes by noting the importance of steganography and expectations for further advancement in the field.
This document discusses steganography techniques for hiding secret information in digital images. It begins with an introduction to steganography and its differences from cryptography. It then discusses various steganography techniques including least significant bit insertion, masking and filtering, and transform domain techniques. It also discusses using bitmap images for steganography and the popularity of formats like JPEG. The goal of the document is to provide an overview of digital image steganography techniques.
This document discusses audio steganography techniques for hiding secret messages in digital audio files. It describes methods such as LSB coding, phase coding, and parity coding that embed data by modifying low-level audio properties that the human ear cannot detect. These techniques exploit properties of the human auditory system to covertly embed messages. The document also covers applications of audio steganography and compares advantages like confidentiality with disadvantages like potential password leakage.
This document discusses digital steganography, which is the process of hiding secret information within other non-secret files like images, text, audio, or video. It describes the history of steganography and some common techniques used, such as least significant bit modification in images and altering text features. The document also compares steganography to cryptography and digital watermarking, outlines some types and uses of steganography, and discusses steganalysis and the future of the field.
A Study of Various Steganographic Techniques Used for Information Hidingijcses
The art of information hiding has received much attention in the recent years as security of information has
become a big concern in this internet era. As sharing of sensitive information via a common communication
channel has become inevitable, Steganography – the art and science of hiding information has gained
much attention. We are also surrounded by a world of secret communication, where people of all types are
transmitting information as innocent as an encrypted credit card number to an online-store and as
insidious as a terrorist plot to hijackers. Steganography derives from the Greek word steganos, meaning
covered or secret, and graphy (writing or drawing) [1]. Steganography is a technology where modern data
compression, information theory, spread spectrum, and cryptography technologies are brought together to
satisfy the need for privacy on the Internet. This paper is an attempt to analyse the various techniques used
in steganography and to identify areas in which this technique can be applied, so that the human race can
be benefited at large.
A brief over overview of steganographical security techniques and how it has been applied, is applied and will continue to be applied in maintaining confidentiality between two communication parties
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.
This document provides an overview of steganography through:
1) Defining steganography and distinguishing it from cryptography by explaining how steganography aims to hide messages within innocent-looking carriers so the message's existence remains concealed.
2) Tracing the evolution of steganography from ancient techniques like invisible ink to modern digital methods.
3) Explaining how steganography embeds messages in carriers like text, images, audio and video and provides an example of hiding text in the least significant bits of image pixel values.
4) Detailing the steps to hide an image using steganography software.
This document discusses steganography, which is a method of hiding secret information within other information. It begins by providing background on the rise of the internet and the need for information security. It then explains steganography and how it differs from cryptography by not only encrypting messages but hiding their very existence. The document outlines various types of steganography, including techniques for hiding messages in text, audio, images, and video files. It notes some advantages and disadvantages of steganography and discusses the latest research on improving steganography detection.
This document provides an overview of steganography and watermarking techniques for hiding information in digital media. It defines steganography as "covered writing" involving hiding secret messages within other digital files like images, audio, or video. Common steganography methods embed data in the least significant bits of pixels or audio samples. Watermarking differs in embedding identifying marks that are robust to modifications and aim to protect copyrights. The document outlines various media and techniques for each, applications, advantages and limitations of both steganography and watermarking.
This document describes an audio steganography technique that aims to increase security by introducing randomness. It discusses how traditional least significant bit (LSB) modification is vulnerable to attacks. The proposed technique randomly selects both the bit position (1st, 2nd, or 3rd LSB) and audio sample for embedding secret message bits. This is intended to prevent attackers from detecting the embedding pattern. The technique uses character encoding like Huffman coding before message bits are hidden in an audio file using the modified LSB method. Experimental results showed the stego audio maintained quality while providing improved security over fixed LSB techniques.
The presentation gives a brief overview and history about steganography and discusses the various types and techniques of steganography.
The types of steganography included are:
Text
Image
Audio
The document provides an overview of steganography, including its definition, history, techniques, applications, and future scope. It discusses different types of steganography such as text, image, and audio steganography. For image steganography, it describes techniques such as LSB insertion and compares image and transform domain methods. It also provides examples of steganography tools and their usage for confidential communication and data protection.
Steganography is a technique for hiding secret messages within other non-secret files like images, audio, or video. It works by embedding messages into the least significant bits of pixel data in an image or by masking digital signatures in portions of audio or video files. The document discusses steganography tools and algorithms for hiding data in images using techniques like LSB insertion and encryption algorithms like DES. It also covers data compression methods like Lempel-Ziv-Welch used for reducing file sizes.
This document discusses a steganography project developed using C# that allows hiding information within image files. It provides an introduction to steganography and its importance. It then describes the software tools used, including C# and various namespaces. It outlines the project scope, requirements, and flowchart. It also includes code analysis and explanations of the encryption and decryption processes used to hide a file within an image without detection. Finally, it discusses future applications and concludes with thanking those who helped with the project.
This document is a project report for a BTech student named Rohit Jaiswal on image and audio steganography. It discusses hiding text messages in digital images and audio files. The report includes an introduction to steganography and cryptography, an analysis of digital images and audio, the design of the steganography system including algorithms, and implementations in C# and Flash. It also covers inputs, outputs, and conclusions with references and source code in the appendix. The supervisor of the project is Mr. Rajeev Srivastava of the Computer Science department at Banaras Hindu University.
The document describes a technical seminar report on an adaptive LSB-OPAP based secret data hiding technique. It was submitted by Tejas.S, an undergraduate student of electronics and communication engineering at Amruta Institute of Engineering and Management Sciences, in partial fulfillment of the requirements for their bachelor's degree. The report discusses a new technique for embedding secret data into color or grayscale images using LSB substitution in an adaptive manner based on pixel intensity ranges. It aims to enhance embedding capacity and imperceptibility compared to traditional LSB techniques.
Steganography is a technique for hiding secret information within ordinary digital files so that the very existence of the hidden information is concealed. It works by replacing bits of redundant data within image, audio, or video files with bits of the secret message. This allows secure communication of hidden information in a way that avoids detection. The document discusses the history and benefits of steganography, providing examples of its use throughout history for covert communication. It also introduces some key concepts and terminology used in modern steganography.
While transferring a file from one point to another through Intranet and Internet we need more file secure concepts. Ordinary, file Encryption-Decryption Concepts, which are readily available in java examples are easily captured by middle way itself. So we need more security combination. This project helps to send a file from one place to another in a secured manner. Firstly the target file is encrypted and it is embedded into an audio or video or any media file. The resultant file will be protected by a password. This resultant media file is not changed in its original format and it can be run in the player, we can’t find any encrypted data inside it. This format will be sent through net. In the destination point it will be retrieved only by our software and giving the relevant password. So it is highly secured.
This document is a self-study seminar report on steganography presented by Abhishek Singh. It includes an introduction to steganography, its importance, and a literature review on different steganographic techniques. The introduction defines steganography as hidden writing and discusses why it is important for secret communications. The literature review covers steganography methods in text, audio, images, and networks. It also discusses steganalysis, applications of steganography, and how it compares to cryptography.
The document discusses data hiding in audio signals using watermarking techniques. It provides an introduction to watermarking and its applications. It describes the basic watermarking system as similar to a communication system. It also discusses speech processing, wavelet analysis, hiding techniques, advantages and disadvantages of audio watermarking. The document presents results from watermarking an audio signal and inserting a speech signal. It concludes that audio watermarking can be used for information tracing, tamper detection and other purposes beyond covert communication.
Steganography is the art of hiding secret messages within other harmless-looking files like images, audio, or video. It embeds data in the least significant bits of pixels in images or within the phase components of audio files, making the hidden messages undetectable without access to the steganographic algorithm and key. Modern steganography techniques include least significant bit insertion and masking/filtering data in image compression algorithms. Steganography differs from cryptography by concealing the existence of secret communication, rather than just scrambling its contents.
This document provides an overview of steganography. It defines steganography as hiding secret messages within other harmless messages to avoid detection by unauthorized parties. Various steganographic techniques are described, including hiding messages in digital files like images, audio, text and network protocols. Detection of steganography (steganalysis) and applications like digital watermarking and tamper proofing are also discussed. The document concludes by noting the importance of steganography and expectations for further advancement in the field.
This document discusses steganography techniques for hiding secret information in digital images. It begins with an introduction to steganography and its differences from cryptography. It then discusses various steganography techniques including least significant bit insertion, masking and filtering, and transform domain techniques. It also discusses using bitmap images for steganography and the popularity of formats like JPEG. The goal of the document is to provide an overview of digital image steganography techniques.
This document discusses audio steganography techniques for hiding secret messages in digital audio files. It describes methods such as LSB coding, phase coding, and parity coding that embed data by modifying low-level audio properties that the human ear cannot detect. These techniques exploit properties of the human auditory system to covertly embed messages. The document also covers applications of audio steganography and compares advantages like confidentiality with disadvantages like potential password leakage.
This document discusses digital steganography, which is the process of hiding secret information within other non-secret files like images, text, audio, or video. It describes the history of steganography and some common techniques used, such as least significant bit modification in images and altering text features. The document also compares steganography to cryptography and digital watermarking, outlines some types and uses of steganography, and discusses steganalysis and the future of the field.
A Study of Various Steganographic Techniques Used for Information Hidingijcses
The art of information hiding has received much attention in the recent years as security of information has
become a big concern in this internet era. As sharing of sensitive information via a common communication
channel has become inevitable, Steganography – the art and science of hiding information has gained
much attention. We are also surrounded by a world of secret communication, where people of all types are
transmitting information as innocent as an encrypted credit card number to an online-store and as
insidious as a terrorist plot to hijackers. Steganography derives from the Greek word steganos, meaning
covered or secret, and graphy (writing or drawing) [1]. Steganography is a technology where modern data
compression, information theory, spread spectrum, and cryptography technologies are brought together to
satisfy the need for privacy on the Internet. This paper is an attempt to analyse the various techniques used
in steganography and to identify areas in which this technique can be applied, so that the human race can
be benefited at large.
A brief over overview of steganographical security techniques and how it has been applied, is applied and will continue to be applied in maintaining confidentiality between two communication parties
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.
This document provides an overview of steganography through:
1) Defining steganography and distinguishing it from cryptography by explaining how steganography aims to hide messages within innocent-looking carriers so the message's existence remains concealed.
2) Tracing the evolution of steganography from ancient techniques like invisible ink to modern digital methods.
3) Explaining how steganography embeds messages in carriers like text, images, audio and video and provides an example of hiding text in the least significant bits of image pixel values.
4) Detailing the steps to hide an image using steganography software.
This document discusses steganography, which is a method of hiding secret information within other information. It begins by providing background on the rise of the internet and the need for information security. It then explains steganography and how it differs from cryptography by not only encrypting messages but hiding their very existence. The document outlines various types of steganography, including techniques for hiding messages in text, audio, images, and video files. It notes some advantages and disadvantages of steganography and discusses the latest research on improving steganography detection.
This document provides an overview of steganography and watermarking techniques for hiding information in digital media. It defines steganography as "covered writing" involving hiding secret messages within other digital files like images, audio, or video. Common steganography methods embed data in the least significant bits of pixels or audio samples. Watermarking differs in embedding identifying marks that are robust to modifications and aim to protect copyrights. The document outlines various media and techniques for each, applications, advantages and limitations of both steganography and watermarking.
This document describes an audio steganography technique that aims to increase security by introducing randomness. It discusses how traditional least significant bit (LSB) modification is vulnerable to attacks. The proposed technique randomly selects both the bit position (1st, 2nd, or 3rd LSB) and audio sample for embedding secret message bits. This is intended to prevent attackers from detecting the embedding pattern. The technique uses character encoding like Huffman coding before message bits are hidden in an audio file using the modified LSB method. Experimental results showed the stego audio maintained quality while providing improved security over fixed LSB techniques.
The presentation gives a brief overview and history about steganography and discusses the various types and techniques of steganography.
The types of steganography included are:
Text
Image
Audio
Enhancement of Data Hiding Capacity in Audio SteganographyIOSR Journals
This document discusses enhancing data hiding capacity in audio steganography. It begins by introducing steganography and its use of hiding secret information in carrier files like images, audio and video. Specifically for audio steganography, it hides data in the least significant bits of audio files. The document proposes a method to enhance capacity by using the last 4 least significant bits instead of just 1, allowing more data to be hidden. It describes the basic process of audio steganography including embedding a secret message into an audio file using a key, and then extracting the message from the stego file at the receiving end.
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
Psychoacoustic Approaches to Audio Steganography Report Cody Ray
This paper explores methods of audio steganography with emphasis on psychoacoustic approaches. Specifically, it describes a project that had the requirement of hiding a text-based message inside an audio signal with minimal or no distortion of the signal as perceived by the human ear. The theory and experimental results of each approach are discussed.
This document presents a novel approach for audio steganography that uses two levels of security. The first level uses an improved RSA encryption algorithm (RPrime RSA) to encrypt a message. The encrypted message is then encoded into an audio file using a genetic algorithm (GA) based least significant bit (LSB) algorithm. The encrypted message bits are embedded into random higher LSB layers of the audio samples to increase robustness against attacks. Genetic algorithm operators are used to minimize bit-level deviations between the original audio and stego audio, improving transparency. The proposed approach claims to provide higher security, capacity, and robustness for hidden data compared to traditional LSB encoding methods.
This document provides an overview of steganography, including its history and various techniques. It discusses steganography in text, images, and audio files. Text steganography can hide messages by making minor spelling or grammatical changes. Image steganography embeds data in the least significant bit of image pixels. Audio steganography utilizes properties of human hearing to conceal messages. The document also covers applications, tools, and the future potential of steganography.
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.
This document summarizes an academic paper on audio steganography techniques using discrete wavelet transformation (DWT) and discrete cosine transformation (DCT). It begins with an abstract of the paper and an introduction to steganography. It then covers classifications of steganography, advantages and disadvantages of audio steganography, applications, and requirements of efficient steganography techniques. Finally, it discusses features of the human auditory system important for audio steganography and describes various audio steganography techniques in both the time and frequency domains.
The document summarizes an improved steganography technique called Bit Plane Complexity Segmentation (BPCS) Steganography. BPCS overcomes limitations of traditional techniques by hiding secret data in the bit-planes of an image vessel. It takes advantage of the human vision system's inability to perceive shape information in complicated binary patterns. The technique replaces noise-like regions of the image bit-planes with secret data without deteriorating image quality. The document describes the BPCS technique, including segmenting images into informative and noise regions, hiding data in the noise regions, and techniques to increase hiding capacity such as conjugating less complex data blocks. It also proposes a web-based BPCS system to provide improved data security and prevent hacking
This document summarizes an algorithm for hiding text within wave audio files. It begins by providing background on steganography and different techniques for embedding information, including within images, video, and audio files. It then describes the structure of digital audio files and how the least significant bit (LSB) manipulation technique works. The proposed algorithm embeds secret binary text into wave files by adding or subtracting a value from quantized phase coefficients of voiced audio blocks, depending on whether the secret bit is a 0 or 1. During extraction, the secret bits are reconstructed by determining whether the phase coefficient is greater than or less than the original quantized value. The algorithm is implemented in two phases - an embedding phase that hides the secret data in
HIDING A MESSAGE IN MP3 USING LSB WITH 1, 2, 3 AND 4 BITSIJCNCJournal
This document summarizes a research paper that proposes a new steganography method for hiding text messages in MP3 audio files. The method randomly selects positions in the MP3 file to embed bits of the text message using the least significant bit (LSB) technique. The text message is embedded starting and ending with a unique signature or key. The methodology focuses on embedding one, two, three or four bits from the secret message into the MP3 file using LSB. The performance is evaluated based on robustness, imperceptibility and hiding capacity. Experimental results show the new method provides increased security compared to other LSB steganography methods.
Steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. It works by hiding secret information in places where an observer would not easily notice. Historically, steganography techniques have included tattooing messages on shaved heads and using invisible ink between the lines of otherwise normal text. Modern methods embed data in the least significant bits of images, audio files, or video frames. Steganography provides a way to secretly communicate while avoiding detection from those not intended to receive the message.
Hungarian-Puzzled Text with Dynamic Quadratic Embedding SteganographyIJECEIAES
Least-Significant-Bit (LSB) is one of the popular and frequently used steganography techniques to hide a secret message in a digital medium. Its popularity is due to its simplicity in implementation and ease of use. However, such simplicity comes with vulnerabilities. An embedded secret message using the traditional LSB insertion is easily decodable when the stego image is suspected to be hiding a secret message. In this paper, we propose a novel secure and high quality LSB embedding technique. The security of the embedded payload is employed through introducing a novel quadratic embedding sequence. The embedding technique is also text dependent and has non-bounded inputs, making the possibilities of decoding infinite. Due to the exponential growth of and quadratic embedding, a novel cyclic technique is also introduced for the sequence that goes beyond the limits of the cover medium. The proposed method also aims to reduce the noise arising from embedding the secret message by reducing bits changed. This is done by partitioning the cover medium and the secret message into N partitions and artificially creating an assignment problem based on bit change criteria. The assignment problem will be solved using the Hungarian algorithm that will puzzle the secret message partition for an overall least bit change.
This document discusses different methods of information hiding including steganography, cryptography, and watermarking. It provides examples of steganography throughout history from ancient times to modern microdot technology. Various steganography tools and file types are listed along with encoding and decoding steps. Common audio and video steganography techniques like LSB coding and parity coding are explained. Potential users of steganography like intelligence agencies and applications like cyber forensics are mentioned. The future scope of developing more efficient steganalysis techniques is also noted.
ADVANCED LSB TECHNIQUE FOR AUDIO STENOGRAPHYcsandit
This work contributes to the multimedia security fields by given that more protected
steganography technique which ensures message confidentiality and integrity. An Advanced
Least Significant Bit (ALSB) technique is presented in order to meet audio steganography
requirements, which are imperceptibility, capacity, and robustness. An extensive evaluation
study was conducted measuring the performance of proposed NLSB algorithm. A set of factors
were measured and used during evaluation, this includes; Peak Signal to Noise Ratio (PSNR)
and Bit Error Rate. MP3 Audio files from five different audio generators were used during
evaluation. Results indicated that ALSB outperforms standard Least Significant Bit (SLSB)
technique. Moreover, ALSB can be embedding an utmost of 750 kb into MP3 file size less than 2
MB with 30db average achieving enhanced capacity capability.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
DATA HIDING IN AUDIO SIGNALS USING WAVELET TRANSFORM WITH ENHANCED SECURITYcsandit
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.
Similar to Hiding text in audio using lsb based steganography (20)
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Hiding text in audio using lsb based steganography
1. Information and Knowledge Management www.iiste.org
ISSN 2224-5758 (Paper) ISSN 2224-896X (Online)
Vol 2, No.3, 2012
Hiding Text in Audio Using LSB Based Steganography
K.P.Adhiya Swati A. Patil
CSE Dept. SSBT’s COET Bambhori,Jalgaon,Bambhori,India
Email: swati.patil251@gmail.com
Abstract
A Steganographic method for embedding textual information in WAV audio is discussed here. In the proposed
method each audio sample is converted into bits and then the textual information is embedded in it. In embedding
process , first the message character is converted into its equivalent binary. The last 4 bits of this binary is taken
into consideration and applying redundancy of the binary code the prefix either 0 or 1 is used. To identify the
uppercase, lower case, space ,and number the control symbols in the form of binary is used. By using proposed
LSB based algorithm, the capacity of stego system to hide the text increases. The performance evaluation is done
on the basis of MOS by taking 20 samples and comparison of SNR values with some known and proposed
algorithm.
Keywords: LSB, WAV, MOS , control symbols, stego system , SNR.
1. Introduction
As the need of security increases only encryption is not sufficient. So stegnograpghy is the supplementary to
encryption. It is not the replacement of encryption. But Steganography along with encryption gives more security
to data. The word steganography is of Greek origin and means "concealed writing" from the Greek words stegnos
meaning "covered or protected", and graphei meaning "writing". Steganography is the technique to hide the
information in some media so that third party can’t recognize that information is hidden into the cover media.. That
media may be text, image ,audio or video. The information that to be hidden is called stego and the media in which
the information is hidden is called host. The stego object can be text, image, audio or video. When the information
is hidden into the audio then it is called Audio steganography. The process of Steganography is as shown in
Figure1. The random selection of the samples used for embedding introduces low power additive white Gaussian
noise (AWGN). It is well known from psychoacoustics literature [3] that the human auditory system (HAS) is
highly sensitive to the AWGN.
Hiding information into a media requires following elements [6]
• The cover media(C) that will hold the hidden data
• The secret message (M), may be plain text, cipher text or any type of data
• The stego function (Fe) and its inverse (Fe-1)
• An optional stego-key (K) or password may be used to hide and unhide the message.
K K
C C
S
Fe Fe-1
M M
Figure 1: The Steganographic operation
Because the size of the information is generally quite small compared to the size of the data in which it must be
hidden (the cover text), electronic media is much easier to manipulate in order to hide data and extract messages.
Secondly, extraction itself can be automated when the data is electronic, since computers can efficiently
manipulate the data and execute the algorithms necessary to retrieve the messages. Because degradation in the
perceptual quality of the cover object may leads to a noticeable change in the cover object which may leads to the
failure of objective of steganography.
If in one application we want to achieve confidentiality, than we have two alternatives: encryption or
steganographic techniques for protection against detection (see Figure 2).
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2. Information and Knowledge Management www.iiste.org
ISSN 2224-5758 (Paper) ISSN 2224-896X (Online)
Vol 2, No.3, 2012
Confidentiality
Steganography Encryption
(Hide existence of the secret (encrypt the message ,but do not
message do not use encryption) hide the message)
Figure 2 Achieving confidentiality
An effective steganographic scheme should posses the following desired characteristics [9]:
Secrecy: A person should not be able to extract the covert data from the host medium without the knowledge of
the proper secret key used in the extracting procedure.
Imperceptibility: The medium after being embedded with the covert data should be indiscernible from the
original medium. One should not become suspicious of the existence of the covert data within the medium.
High capacity: The maximum length of the covert message that can be embedded should be as long as
possible.
Resistance: The covert data should be able to survive when the host medium has been manipulated, for example
by some lossy compression scheme .
Accurate extraction: The extraction of the covert data from the medium should be accurate and reliable.
Basically, the purpose of steganography is to provide secret communicate like cryptography.
2.Audio Steganography
Like the document images, the sound files may be modified in such a way that they contain hidden information,
like copyright information; those modifications must be done in such a way that it should be impossible for a pirate
to remove it, at least not without destroying the original signal. The methods that embeds data in sound files use the
properties of the Human Auditory System (HAS). The HAS perceives the additive random noise and also the
perturbations in a sound file can also be detected. But there are some “holes” we can exploit. While the HAS have
a large dynamic range, it has a fairly small differential range.
2.1Technique for Data Hiding in Audio
There are four techniques for hiding data in Audio as following:
Amplitude
2.1.1 Least Significant Bit (LSB) Encoding:
Least significant bit (LSB) coding is the simplest way to embed information in a digital audio file. By
substituting the least significant bit of each sampling point with a binary message, LSB coding allows for a large
amount of data to be encoded. In LSB coding, the ideal data transmission rate is 1 kbps per 1 kHz. In some
implementations of LSB coding, however, the two least significant bits of a sample are replaced with two
message bits. This increases the amount of data that can be encoded but also increases the amount of resulting
noise in the audio file as well. A novel method which increases the limit up to four bits by Nedeljko Cvejic,
Tapio Seppben & mediaTeam Oulu at Information Processing Laboratory, University of Oulu, Finland .[5]
To extract a secret message from an LSB encoded sound file, the receiver needs access to the sequence of
sample indices used in the embedding process. Normally, the length of the secret message to be encoded is
smaller than the total number of samples in a sound file. One must decide then on how to choose the subset of
samples that will contain the secret message and communicate that decision to the receiver. One trivial technique
is to start at the beginning of the sound file and perform LSB coding until the message has been completely
embedded, leaving the remaining samples unchanged. This creates a security problem, however in that the first
part of the sound file will have different statistical properties than the second part of the sound file that was not
modified. One solution to this problem is to pad the secret message with random bits so that the length of the
message is equal to the total number of samples.
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3. Information and Knowledge Management www.iiste.org
ISSN 2224-5758 (Paper) ISSN 2224-896X (Online)
Vol 2, No.3, 2012
There are two main disadvantages associated with the use of methods like LSB coding. The human ear is very
sensitive and can often detect even the slightest bit of noise introduced into a sound file, Second disadvantage
however, is that this is not robust. If a sound file embedded with a secret message using either LSB coding was
resample, the embedded information would be lost. Robustness can be improved somewhat by using a redundancy
technique while encoding the secret message. However, redundancy techniques reduce data transmission rate
significantly.
2.1.2 Phase Coding
Phase coding addresses the disadvantages of the noise inducing methods of audio steganography. Phase coding
relies on the fact that the phase components of sound are not as perceptible to the human ear as noise is. Rather than
introducing perturbations, the technique encodes the message bits as phase shifts in the phase spectrum of a digital
signal, achieving an inaudible encoding in terms of signal-to perceived noise ratio. Original and encoded signal are
as shown in Figure 3.
Time, t
Cover Shifted
Signal Cover
Figure 3. illustrate the original cover signal and encoded shifted signal of phase coding technique.
Phase coding is explained in the following procedure:
• The original sound signal is broken up into smaller segments whose lengths equal the size of the message
to be encoded.
• A Discrete Fourier Transform (DFT) is applied to each segment to create a matrix of the phases and
Fourier transform magnitudes.
• Phase differences between adjacent segments are calculated.
• Phase shifts between consecutive segments are easily detected. In other words, the absolute phases of the
segments can be changed but the relative phase differences between adjacent segments must be
preserved. Therefore the secret message is only inserted in the phase vector of the first signal segment as
follows:
π / 2 if message bit =1
Phase_new = π / 2 if message bit
=0
• A new phase matrix is created using the new phase of the first segment and the original phase differences.
• Using the new phase matrix and original magnitude matrix, the sound signal is reconstructed by applying
the inverse DFT and then concatenating the sound segments back together.
To extract the secret message from the sound file, the receiver must know the segment length. The receiver can
then use the DFT to get the phases and extract the information. One disadvantage associated with phase coding is a
low data transmission rate due to the fact that the secret message is encoded in the first signal segment only. This
might be addressed by increasing the length of the signal segment. However, this would change phase relations
between each frequency component of the segment more drastically, making the encoding easier to detect. As a
result, the phase coding method is used when only a small amount of data, such as a watermark, needs to be
concealed.
2.1.3 Echo Hiding
In echo hiding, information is embedded in a sound file by introducing an echo into the discrete signal. Like the
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spread spectrum method, it too provides advantages in that it allows for a high data transmission rate and provides
superior robustness when compared to the noise inducing methods. To hide the data successfully, three parameters
of the echo are varied: amplitude, decay rate, and offset (delay time) from the original signal. All three parameters
are set below the human hearing threshold so the echo is not easily resolved. In addition, offset is varied to
represent the binary message to be encoded. If only one echo was produced from the original signal, only one bit of
information could be encoded. Therefore, the original signal is broken down into blocks before the encoding
process begins. Once the encoding process is completed, the blocks are concatenated back together to create the
final signal. To extract the secret message from the stego-signal, the receiver must be able to break up the signal
into the same block sequence used during the encoding process. Then the autocorrelation function of the signal's
cepstrum (the cepstrum is the Forward Fourier Transform of the signal's frequency spectrum) can be used to
decode the message because it reveals a spike at each echo time offset, allowing the message to be reconstructed.
For a discrete signal f(t), an echo f(t-dt), with some delay can be introduced to produce the stego signal s(t)=f(t) +
f(t-dt)[2].
2.1.4 Spread Spectrum
In the context of audio steganography, the basic spread spectrum (SS) method attempts to spread secret
information across the audio signal's frequency spectrum as much as possible. This is analogous to a system using
an implementation of the LSB coding that randomly spreads the message bits over the entire sound file. However,
unlike LSB coding, the SS method spreads the secret message over the sound file's frequency spectrum, using a
code that is independent of the actual signal. As a result, the final signal occupies a bandwidth in excess of what is
actually required for transmission. Two versions of SS can be used in audio steganography: the direct-sequence
and frequency-hopping schemes. In direct sequence SS, the secret message is spread out by a constant called the
chip rate and then modulated with a pseudorandom signal. It is then interleaved with the cover-signal. In
frequency-hopping SS, the audio file's frequency spectrum is altered so that it hops rapidly between frequencies.
The SS method has the potential to perform better in some areas than LSB coding, parity coding, and phase coding
techniques in that it offers a moderate data transmission rate while also maintaining a high level of robustness
against removal techniques. However, the SS method shares a disadvantage with LSB and parity coding in that it
can introduce noise into a sound file.
3.Proposed Technique
The existing algorithm hided the text in image. In proposed technique the algorithm will be implemented for
Audio Signal to hide text.
The algorithm based on the redundancy of bits in binary code of numbers, lowercase and uppercase alphabets.
If we look at the binary code of numbers from 0 to 9, A to O, O to P, a to o and a to p the last 4 bits are different and
first 4 bit are similar. So any number and alphabet can be represented by the last 4 bits and adding either ‘0’ or ‘1’
at the first position. To differentiate whether the character is number, uppercase alphabet or lowercase alphabet
control symbols are used which is of the same type as that of number or alphabet.
For special symbols like !, “ , # , $ , %, & , ( , , ) , *, + , ‘, - , . , / is also observed and these special symbols
can also be embedded in WAV file.
When embedding the textual information in any audio file, first the audio signal is converted
into bits. Then the message to be embedded is converted from above strategy[4]. By applying LSB
algorithm, the message is embedded into 16 bits or 8 bits audio sample. The performance is evaluated
by applying LSB algorithm at different position i.e 1LSB, 2LSB and so on. At the receiver side,
the first five bytes are taken, if these bytes are same as our control symbols bytes then the next character case is
defined.
Encoding Algorithm and Decoding Algorithm
Encoding Algorithm
1. Input the text to be embedded.
2. Convert the text into 5 bit code by checking the redundancy in binary code of alphabets and numbers.
3. Read WAV audio file as cover file.
4. Select audio sample and hide the converted 5 bit code of the text in WAV file using LSB algorithm.
5. Repeat till the whole message can be embedded in audio.
Decoding Algorithm
1. Read the stego-object i.e. cover audio after encoding.
2. Extract the message by reading the control symbols in samples and reading LSB.
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3. Select all samples and store all LSB position bits in array.
4. Divide the array into number of rows and columns
5. Display the secret message.
4. Experiment
This Steganpgraphy is implemented in Matlab 7. To measure the performance of proposed method , MOS (Mean
Opinion Score strategy is used. Mean value is calculated by asking people about the difference in the original wav
file and embedded wav file.This rating is done on 5 point scale. The LSB algorithm is tested for 1, 2, 3,4 ,5,6,7LSB
position.
In order to evaluate the sound quality after embedding the secret image into audio files, two type of test are
carried out: MOS and signal to noise ratio w.r.t. sample size, sample rate, bitrate, size of audio.
4.1 Mean Opinion Score (MOS)
Subjective quality evaluation for the text hiding in audio has been done by listening tests involving twenty persons.
The audio files are categorized as per number of bits per sample, number of channels. The entire tests have been
carried out at each category of sound files. Initially in the first part repeatedly presented the audio clips with hidden
text and audio clips without hidden text into it, in random order to the listeners. Listeners were asked to determine
which one is the audio with text hidden in it and without it. Here we have also calculate the mean opinion score of
audio where different LSB positions of audio file i.e. simply 1 or 2 or 3 or 4 or 5 th LSB positions, and up to fourth
4th LSB positions were used for hiding image into it. To calculate the mean opinion score, five point scales is used
by the individual after listening the music file and final mean of all scores is M.O.S. Mean Opinion Score for all
four categories of sound are as shown in Table1. This five-point scale is defined in the following manner as given
under, using a 5-point impairment scale:
5: Imperceptible
4: Slightly Perceptible but not noisy
3: Slightly noisy
2: Noisy
1: Very Noisy
Table1 shows that for 8 bit Brake wav file, 16 bit with 1 mono channel Originalrekam2 sound file, 16 bit 2 channel
windows Shutdown audio file and 16 bit handel sound. Form table 1 proposed algorithm is close for 16 bit audio.
For 8 bit audio with 1 mono channel, 11 KHz the algorithm is closer up to 5 LSB, for 16 bit audio with 1 mono
channel ,22 KHz result is close up to 7 LSB, for 16 bit audio with mono channel,8Khz sample rate the best result
up to 3 LSB and for 16 bit , 2 channel, 22 KHz sound the result is good upto 5 LSB. Using the statistical tool
SPSS the mean value for 4 types of sounds up to 5 bits LSB are calculated as shown in table1.Table 2 shows the
SNR for the audio samples and table 3 shows the comparison of SNR values with some known algorithm. [7]
Table1: MOS for Proposed algorithm
MOS at 1,2,3,4,5,6,7 LSB Position
Methods Brake Originalrekam2 Handel Shutdown
1 LSB 4.3 3.9 4.3 4.0
2 LSB 4.6 4.3 4.3 4.0
3 LSB 4.1 4.1 4.3 4.0
4 LSB 4.0 4.0 4.1 3.9
5 LSB 3.9 4.2 4.1 4.0
6 LSB 3.9 4.2 4 4.0
7 LSB 3.5 4.2 4 4.0
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The second approach for testing is the quality evaluation using signal to noise ratio. For above 4 samples SNR is as
shown in the following table. SNR is calculated by following formulae [8].
Mer (mean error rate) = coverfilebits – embeddedfilebits / coverfilebits
sizeinfo=size of the cover file
snr=(20*log10(sizeinfo / mer))
Table 2 : SNR for 4 sample audio
Audio SNR Size Sample size Sample rate
Brake 58.33 5781 8 11
Handel 73.75 73113 16 22
Originalrekam2 69.36 44100 16 22
WindowsXP shutdown 73.45 70641 16 22
Table 3: Performance Comparison with some known and similar technique
Researchers/ Algorithm SNR
Pooyan and Delforouzi 39
Cvejik and Sepannen 42
Cvejik and Sepannen 39
Bao and Ma 36
Mansour Sheikhan 49
Proposed Algorithm 68
To see the result in terms of number of bytes required the 4 samples of 16 bit WAV audio is taken and its number
of bytes to hide the text is measured. e.g To hide 5 character ordinary algorithm takes 5*8=40 bytes in Matlab and
proposed algorithm takes 5*5=25 bytes.
5. Conclusion
In the proposed steganographic system, 16bitWAV and 8bitWAVaudio file are supported and the secret message
can be hidden in the audio file with less storage capacity. The existing system requires the large storage capacity as
the message is stored as it is ,so the proposed method requires less storage capacity as it requires less storage
space instead of 8 bit code. Proposed algorithm gives better result for 16 bit wav audio as compared to 8 bit .
References
[1]C. Yeh, C. Kuo, (October 1999)Digital Watermarking Through Quasi M- Arrays, Proc. IEEE Workshop On
Signal Processing Systems, Taipei, Taiwan, Pp. 456-461.
[2] Dr. D Mukhopadhyay, A Mukherjee, S Ghosh, S Biswas, P Chakarborty (2005.) An Approach for
Message Hiding using Substitution Techniques and Audio Hiding in Steganography, IEEE
[3] E. Zwicker, “Psychoacoustics”, Springer Verlag, Berlin, 1982.
[4] Mazen Abu Zaher Modified Least Significant Bit (MLSB) Published by Canadian Center of Science and
Education Vol. 4, No. 1; January 2011
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Vol 2, No.3, 2012
[5] Nedeljko Cvejic, Tapio Seppben(,IEEE 2002) Increasing The Capacity Of LSB-Based Audio Steganography
[6] Steganographic Techniques and their use in an Open-Systems Environment- Bret Dunbar, The information
Security reading Room, SANS Institute 2002http://www.sans.org/reading room/whitepapers/covert/677.php
[7]Mansour Sheikhan et al, High Quality Audio steganography by Floating Substitution of LSBs in Wavelet
Domain, world applied science Journal IDOSI publications , 2010
[8] Y.V.N.Tulasi et al.,Steganography -Security through Images
[9] On Embedding of Text in Audio – A case of Steganography Pramatha Nath Basu, 2010
International Conference on Recent Trends in Information, Telecommunication and Computing
K.P.Adhiya is an associate professor and HOD of Computer science and engineering department of SSBT’s
COET Bambhori Jalgaon, Maharshtra, India. He has twenty one years teaching experience and pursuing PhD
from North Maharashtra University, Jalgaon.
Swati A. Patil is a research scholar in SSBT’s COET Bambhori. She has seven years of teaching experience.
Currently she is working as lecturer in G. H. Raisoni Insitute of Information Technology, Jalgaon.
14
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