05-11-2016, 03:52 PM
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Abstract— In recent years, the field of steganography has become very popular and a lot of research is being done in this field. Reversible data hiding (RDH) is a technique, by which the original cover can be losslessly recovered. All previous methods hide the data by vacating room after encryption. In this paper, we use a novel method of vacating room before encryption with a traditional RDH algorithm. The proposed method provides real reversibility, that is, data extraction and image recovery. In the proposed method along with image, we also use video for data hiding. This method provides improvised PSNR values.
Introduction
With the development of computer and its expansion in different areas of life and work, the issue of information security has become increasingly important. One of the grounds discussed in information security is the exchange of information through the cover media. To this end, different methods such as cryptography, steganography, coding, etc have been used. Steganography is a way of hiding secret messages into innocent looking cover documents, such as digital images and audios. In today's world there are more versatile and practical covers for hiding messages– digital documents, images, video, and audio files. As long as an electronic document contains irrelevant or redundant information, it can be used as a “cover” to hide secret messages.
Reversible data hiding (RDH) is a steganographic technique in which secret message are hidden within a cover media such that the existence of the message is concealed and the cover media is retrieved without any distortion after extracting the embedded data. It is necessary to restore the cover media for some medical and legal applications. The advantage of steganography over cryptography is that just enciphering of data does not provide adequate security so focus has turned towards the method of hiding the existence of the message rather than just enciphering them [1]. The cover media can be an image, audio or a video file. In this paper, 8- bit grayscale image is used as the cover media referred to as “Cover Image”. The cover image with secret data embedded in it is referred to as “Marked Image”
Many reversible data hiding algorithm based on least significant bit (LSB) substitution and histogram shifting (HS) have been proposed in the literature. One of the most common LSB based data hiding technique is to substitute the LSB of the cover image with the message bits. A LSB substitution based data hiding scheme that uses optimal pixel adjustment was proposed by Chan et al. [2] the LSB method of data hiding achieves a high embedding capacity with low computational overhead. The major disadvantage of LSB based data hiding is that at higher payload they can be susceptible to R-S steganalysis [3]. A histogram shifting based reversible data hiding technique proposed by Ni et al. [4] uses the pixel value that has maximum occurrence in a cover image to embed data in this data hiding approach each pixel is incremented or decremented by one, there by preserving the statistical quality of the marked image. Tai et al [5] proposed a scheme based on histogram modification of pixel difference, the spatial correlation of an image is exploited here, and the distribution of pixel difference is used to achieve high embedding capacity. Ni et al.’s histogram shifting scheme [4] is used here to avoid the problem of overflow and underflow. In [6] Li et al. proposes a HS based RDH scheme which is an extension of Ni et al. [4] and Lee et al. [7] where the data is embedded in cover image by designing a specific shifting and a embedding function, In this method the image is divided into blocks and an n dimensional histogram is obtained by counting the frequency of occurrence of pixel value. The data is embedded by modifying the n-dimensional histogram. One of the major advantages of HS based RDH scheme is that modification in the pixel value can be controlled and it can be ensured that the statistical quality of the cover image is preserved. In this paper the performance of LSB substitution based RDH and the proposed HS-RDH algorithm based on grayscale division are compared with respect to embedding rate and peak signal to noise ratio (PSNR).
Related work
The previous method can be summarized as the framework in which we are vacating room after encryption (VRAE) .In this content owner encrypts the original image using standard cipher with encryption key. There are a few techniques by which we are vacating the room after encryption.
1. Fridich et al[8] constructed a general framework for RDH for vacating room in encrypted image. By first extracting compressible features of original image and then compressing them losslessly. In this way space can be created for embedding data.
2. Another method is based on difference expansion (DE) [9], for vacating room in encrypted image in which the difference of each pixel group is expanded, e.g., multiplied by 2, and thus the least significant bits (LSBs) of the difference are all-zero and the space created can be used for embedding data.
3. Another method is histogram shift (HS) [10], for vacating room in encrypted image in which space is saved for data embedding by shifting the bins of histogram of gray values. and the space created can be used for embedding data.
The methods explained above are used for vacating the space from encrypted image for embedding data. After vacating room by creating space in the image the content owner encrypts the original image using a standard cipher with an encryption key. After producing the encrypted image, the content owner hands over
All the three methods discussed above to vacate room from the encrypted version of images directly. Because the entropy of encrypted images has been maximized, these techniques can achieve only a small payloads, or generate marked image with poor quality for large payload and all of them are subject to some error rates on data extraction and/or image restoration. Although the methods in, can eliminate errors by error correcting codes, the pure payloads will be further consumed
Proposed method
Here the content owner first reserves enough
space on original image which is termed as reserving room and then converts the image into its encrypted version with the encryption key. Now, the data embedding process in encrypted images is inherently reversible for the data hider only needs to accommodate data into the spare space previous emptied out. The data extraction and image recovery are identical to that of Framework VRAE. Standard RDH algorithms are the ideal operator for reserving room before encryption and can be easily applied to Framework RRBE to achieve better performance compared with techniques from
Framework VRAE.
We elaborate a practical method based on the Framework “RRBE”, which primarily consists of four stages: generation of encrypted image, data hiding in encrypted image, data extraction and image recovery. Here the image can be replaced by a video; thereby we can select one of the frames of the video and then send required text data in one of the frames. By using this method we will be able to send more data as there will be large no of frames in a video.
Algorithm steps:
1. The image or the video frame in which the data to be hidden are read.
2. The image is divided into two planes.
3. The image then undergoes the process of histogram shifting, so that spaces are vacated before encryption.
4. The data in the form of text or image are read and then converted to binary.
5. The process of image encryption is done using a standard algorithm.
6. The data is embedded into the vacated space using the data hiding key.
7. At the receiving end, the decryption of image is done using the key.
8. The extraction of data is done using the data decrypting key.
9. The results are displayed, for video and image.
Software Specifications
A. Language Used, MATLAB 7.5
1) Software Description: If you are new to MATLAB, you should start by reading Manipulating Matrices. The most important things to learn are how to enter matrices, how to use the: (colon) operator, and how to invoke functions. After you master the basics, you should read rest of the sections below
and run the demos.
At the heart of MALTAB is a new language you must learn before you can fully exploit its power. You can learn the basis of MATLB quickly, and mastery comes shortly after. You will be rewarded with high productivity, high creativity computing power that will change the way you work
2) Introduction: MATLAB is a high performance language for technical computing. It integrates
computation, visualization, and programming in an easy-to-use environment where problems and
solution are expressed in familiar mathematical notation.
MALTAB is an interactive system whose basic data element is an array that does not require
dimensioning. This allows you want solve many technical computing problems, especially those with matrix and vector formulations, in especially those with matrix and vector formulation, in a fraction of the time it would take to write a program in a scalar non interactive languages such as C or
FORTRAN.
The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access
to matrix software developed by the LINPACK and EISPACK projects. Today, MATLAB uses software developed by the LAPACK and ARPACK projects, which together represent the state of- the-art in software for matrix computation.
MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high productivity research, development,
and analysis.
MATLAB features a family of application-specific solution called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB Environment to solve particular classes of problems. Areas in
which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others.
3) GUI: A graphical user interface (GUI) is a user interface built with graphical objects, such as buttons, text fields, sliders, and menus. In general, these objects already have meanings to most computer users. For example when you move a slider, a value changes; when you press an OK button, your settings are applied and the dialog box is dismissed. Of course, to leverage this built- in familiarity; you must be consistent in how you use the various GUI building components. Applications that provide GUIs are generally easier to learn and use since the person using the application does not need to know what commands are available or how they work.
5. Future Work
Steganography is one of the most powerful techniques to conceal the existence of hidden secret data
inside a cover object. Images are the most popular cover objects for steganography, and thus the
importance of image steganography. Embedding secret information inside images requires intensive
computations, and therefore, designing steganography in hardware speeds up steganography. This
work presents a hardware design of Least Significant Bit (LSB) steganography technique in a
SPARTAN3 FPGA. The design utilizes the embedded processor as well as specialized logic to
perform the steganography steps. Here the core processor Microblaze is design, implement using XILINX ISE 10.1 AND XILI8NX PLATFORM STUDIO. Design suite the algorithm is write in
system C Language and test in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC
using the RS232 cable. Some of the Advantages of this approach are that its flexibility allows the user to balance the required performance of the target application against the logic area cost. The user can tailor the processor with or without advance features, based on the budget of hardware. This can be done on a pipelined architecture, so can increase speed and reduce processing time.
6. Conclusion
In this a data hiding method by LSB substitution process is proposed. Simulation result shows the
effectiveness of the proposed method. A good balance between the security and the image quality is
achieved. In the proposed algorithm, number of steps less. Thus, the computational complexity is
reduced. Algorithm is usage for both 8 bit and 24 bit image of cover and secret image, so it is easy to
be implementing in both gray scale and color image. Benefited from the effective optimization, a
good balance between the security and the image quality is achieved. The future work will focus on
improving the efficiency and speed of calculation by using pipelined architecture.
Abstract— In recent years, the field of steganography has become very popular and a lot of research is being done in this field. Reversible data hiding (RDH) is a technique, by which the original cover can be losslessly recovered. All previous methods hide the data by vacating room after encryption. In this paper, we use a novel method of vacating room before encryption with a traditional RDH algorithm. The proposed method provides real reversibility, that is, data extraction and image recovery. In the proposed method along with image, we also use video for data hiding. This method provides improvised PSNR values.
Introduction
With the development of computer and its expansion in different areas of life and work, the issue of information security has become increasingly important. One of the grounds discussed in information security is the exchange of information through the cover media. To this end, different methods such as cryptography, steganography, coding, etc have been used. Steganography is a way of hiding secret messages into innocent looking cover documents, such as digital images and audios. In today's world there are more versatile and practical covers for hiding messages– digital documents, images, video, and audio files. As long as an electronic document contains irrelevant or redundant information, it can be used as a “cover” to hide secret messages.
Reversible data hiding (RDH) is a steganographic technique in which secret message are hidden within a cover media such that the existence of the message is concealed and the cover media is retrieved without any distortion after extracting the embedded data. It is necessary to restore the cover media for some medical and legal applications. The advantage of steganography over cryptography is that just enciphering of data does not provide adequate security so focus has turned towards the method of hiding the existence of the message rather than just enciphering them [1]. The cover media can be an image, audio or a video file. In this paper, 8- bit grayscale image is used as the cover media referred to as “Cover Image”. The cover image with secret data embedded in it is referred to as “Marked Image”
Many reversible data hiding algorithm based on least significant bit (LSB) substitution and histogram shifting (HS) have been proposed in the literature. One of the most common LSB based data hiding technique is to substitute the LSB of the cover image with the message bits. A LSB substitution based data hiding scheme that uses optimal pixel adjustment was proposed by Chan et al. [2] the LSB method of data hiding achieves a high embedding capacity with low computational overhead. The major disadvantage of LSB based data hiding is that at higher payload they can be susceptible to R-S steganalysis [3]. A histogram shifting based reversible data hiding technique proposed by Ni et al. [4] uses the pixel value that has maximum occurrence in a cover image to embed data in this data hiding approach each pixel is incremented or decremented by one, there by preserving the statistical quality of the marked image. Tai et al [5] proposed a scheme based on histogram modification of pixel difference, the spatial correlation of an image is exploited here, and the distribution of pixel difference is used to achieve high embedding capacity. Ni et al.’s histogram shifting scheme [4] is used here to avoid the problem of overflow and underflow. In [6] Li et al. proposes a HS based RDH scheme which is an extension of Ni et al. [4] and Lee et al. [7] where the data is embedded in cover image by designing a specific shifting and a embedding function, In this method the image is divided into blocks and an n dimensional histogram is obtained by counting the frequency of occurrence of pixel value. The data is embedded by modifying the n-dimensional histogram. One of the major advantages of HS based RDH scheme is that modification in the pixel value can be controlled and it can be ensured that the statistical quality of the cover image is preserved. In this paper the performance of LSB substitution based RDH and the proposed HS-RDH algorithm based on grayscale division are compared with respect to embedding rate and peak signal to noise ratio (PSNR).
Related work
The previous method can be summarized as the framework in which we are vacating room after encryption (VRAE) .In this content owner encrypts the original image using standard cipher with encryption key. There are a few techniques by which we are vacating the room after encryption.
1. Fridich et al[8] constructed a general framework for RDH for vacating room in encrypted image. By first extracting compressible features of original image and then compressing them losslessly. In this way space can be created for embedding data.
2. Another method is based on difference expansion (DE) [9], for vacating room in encrypted image in which the difference of each pixel group is expanded, e.g., multiplied by 2, and thus the least significant bits (LSBs) of the difference are all-zero and the space created can be used for embedding data.
3. Another method is histogram shift (HS) [10], for vacating room in encrypted image in which space is saved for data embedding by shifting the bins of histogram of gray values. and the space created can be used for embedding data.
The methods explained above are used for vacating the space from encrypted image for embedding data. After vacating room by creating space in the image the content owner encrypts the original image using a standard cipher with an encryption key. After producing the encrypted image, the content owner hands over
All the three methods discussed above to vacate room from the encrypted version of images directly. Because the entropy of encrypted images has been maximized, these techniques can achieve only a small payloads, or generate marked image with poor quality for large payload and all of them are subject to some error rates on data extraction and/or image restoration. Although the methods in, can eliminate errors by error correcting codes, the pure payloads will be further consumed
Proposed method
Here the content owner first reserves enough
space on original image which is termed as reserving room and then converts the image into its encrypted version with the encryption key. Now, the data embedding process in encrypted images is inherently reversible for the data hider only needs to accommodate data into the spare space previous emptied out. The data extraction and image recovery are identical to that of Framework VRAE. Standard RDH algorithms are the ideal operator for reserving room before encryption and can be easily applied to Framework RRBE to achieve better performance compared with techniques from
Framework VRAE.
We elaborate a practical method based on the Framework “RRBE”, which primarily consists of four stages: generation of encrypted image, data hiding in encrypted image, data extraction and image recovery. Here the image can be replaced by a video; thereby we can select one of the frames of the video and then send required text data in one of the frames. By using this method we will be able to send more data as there will be large no of frames in a video.
Algorithm steps:
1. The image or the video frame in which the data to be hidden are read.
2. The image is divided into two planes.
3. The image then undergoes the process of histogram shifting, so that spaces are vacated before encryption.
4. The data in the form of text or image are read and then converted to binary.
5. The process of image encryption is done using a standard algorithm.
6. The data is embedded into the vacated space using the data hiding key.
7. At the receiving end, the decryption of image is done using the key.
8. The extraction of data is done using the data decrypting key.
9. The results are displayed, for video and image.
Software Specifications
A. Language Used, MATLAB 7.5
1) Software Description: If you are new to MATLAB, you should start by reading Manipulating Matrices. The most important things to learn are how to enter matrices, how to use the: (colon) operator, and how to invoke functions. After you master the basics, you should read rest of the sections below
and run the demos.
At the heart of MALTAB is a new language you must learn before you can fully exploit its power. You can learn the basis of MATLB quickly, and mastery comes shortly after. You will be rewarded with high productivity, high creativity computing power that will change the way you work
2) Introduction: MATLAB is a high performance language for technical computing. It integrates
computation, visualization, and programming in an easy-to-use environment where problems and
solution are expressed in familiar mathematical notation.
MALTAB is an interactive system whose basic data element is an array that does not require
dimensioning. This allows you want solve many technical computing problems, especially those with matrix and vector formulations, in especially those with matrix and vector formulation, in a fraction of the time it would take to write a program in a scalar non interactive languages such as C or
FORTRAN.
The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access
to matrix software developed by the LINPACK and EISPACK projects. Today, MATLAB uses software developed by the LAPACK and ARPACK projects, which together represent the state of- the-art in software for matrix computation.
MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high productivity research, development,
and analysis.
MATLAB features a family of application-specific solution called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB Environment to solve particular classes of problems. Areas in
which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others.
3) GUI: A graphical user interface (GUI) is a user interface built with graphical objects, such as buttons, text fields, sliders, and menus. In general, these objects already have meanings to most computer users. For example when you move a slider, a value changes; when you press an OK button, your settings are applied and the dialog box is dismissed. Of course, to leverage this built- in familiarity; you must be consistent in how you use the various GUI building components. Applications that provide GUIs are generally easier to learn and use since the person using the application does not need to know what commands are available or how they work.
5. Future Work
Steganography is one of the most powerful techniques to conceal the existence of hidden secret data
inside a cover object. Images are the most popular cover objects for steganography, and thus the
importance of image steganography. Embedding secret information inside images requires intensive
computations, and therefore, designing steganography in hardware speeds up steganography. This
work presents a hardware design of Least Significant Bit (LSB) steganography technique in a
SPARTAN3 FPGA. The design utilizes the embedded processor as well as specialized logic to
perform the steganography steps. Here the core processor Microblaze is design, implement using XILINX ISE 10.1 AND XILI8NX PLATFORM STUDIO. Design suite the algorithm is write in
system C Language and test in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC
using the RS232 cable. Some of the Advantages of this approach are that its flexibility allows the user to balance the required performance of the target application against the logic area cost. The user can tailor the processor with or without advance features, based on the budget of hardware. This can be done on a pipelined architecture, so can increase speed and reduce processing time.
6. Conclusion
In this a data hiding method by LSB substitution process is proposed. Simulation result shows the
effectiveness of the proposed method. A good balance between the security and the image quality is
achieved. In the proposed algorithm, number of steps less. Thus, the computational complexity is
reduced. Algorithm is usage for both 8 bit and 24 bit image of cover and secret image, so it is easy to
be implementing in both gray scale and color image. Benefited from the effective optimization, a
good balance between the security and the image quality is achieved. The future work will focus on
improving the efficiency and speed of calculation by using pipelined architecture.