This paper presents an algorithm in spatial domain which gives less distortion to the cover image during embedding process. Minimizing embedding impact and maximizing embedding capacity are the key factors of any steganography algorithm. Peak Signal to Noise Ratio (PSNR) is the familiar metric used in discriminating the distorted image (stego image) and cover image. Here matrix embedding technique is chosen to embed the secret image which is initially Huffman encoded. The Huffman encoded image is overlaid on the selected bits of all the channels of pixels of cover image through matrix embedding. As a result, the stego image is constructed with very less distortion when compared to the cover image ends up with higher PSNR value. A secret image which cannot be embedded in a normal LSB embedding technique can be overlaid in this proposed technique since the secret image is Huffman encoded. Experimental results for standard cover images, which obtained higher PSNR value during the operation is shown in this paper.
TEXT STEGANOGRAPHY USING LSB INSERTION METHOD ALONG WITH CHAOS THEORYIJCSEA Journal
The art of information hiding has been around nearly as long as the need for covert communication. Steganography, the concealing of information, arose early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret message within a larger image or message such that the hidden message or an image is undetectable; this is in contrast to cryptography, where the existence of the message itself is not disguised, but the content is obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a steganogram while maximizing the size of the payload. Current digital image steganography presents the challenge of hiding message in a digital image in a way that is robust to image manipulation and attack. This paper explains about how a secret message can be hidden into an image using least significant bit insertion method along with chaos.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
A novel secure image steganography method based on chaos theory in spatial do...ijsptm
This paper presents a novel approach of building a secure data hiding technique in digital images. The
image steganography technique takes the advantage of limited power of human visual system (HVS). It uses
image as cover media for embedding secret message. The most important requirement for a steganographic
algorithm is to be imperceptible while maximizing the size of the payload. In this paper a method is
proposed to encrypt the secret bits of the message based on chaos theory before embedding into the cover
image. A 3-3-2 LSB insertion method has been used for image steganography. Experimental results show a
substantial improvement in the Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF) value of the
proposed technique over the base technique of 3-3-2 LSB insertion.
An Image Steganography Algorithm Using Huffman and Interpixel Difference Enco...CSCJournals
Steganography is an art of hiding secret information on a cover medium through imperceptible methodology. The three pillars on which a steganography algorithm should be erected are: Embedding capacity, Imperceptibility and Robustness. It is fortunate that all these goals are interdependent on one another. The state of art is finding an optimum solution that keep up all the steganography goals. It is believed that there is no productivity if the size of cover medium gets extended to meet in housing the secret data on it. This happens due to lack in refinement of embedding algorithm and failing in analyzing the data structure of secret data. In this paper, an attempt has made to improve embedding capacity and bring very less distortion to the cover medium by analyzing the data structure of the payload. A residual coding is carried on the pay load before it is submitted to Huffman encoding which is a lossless compression technique. As a result, the representation of payload had shrink. Further, the variable bit encoding (Huffman) do a lossless compression and finally the payload get housed on the cover medium. This ended with high embedding capacity and less imperceptibility. Peak signal to noise ratio confirms that the residual coding had given improvised results than few existing embedding algorithm.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper on a Tree Based Parity Check (TBPC) scheme for data hiding. The TBPC scheme aims to reduce distortion when hiding data in a cover object like an image. It works by constructing a master tree from the cover object's bits and deriving a master string. The message is then hidden by XORing it with the master string to get a toggle string. A toggle tree is constructed from this and XORed with the master tree to get the stego object. The paper proposes a majority vote strategy for building the toggle tree that uses the minimum number of 1s, reducing distortion. Experimental results show the TBPC scheme effectively hides large payloads with minimal distortion.
Genetic Algorithm based Mosaic Image Steganography for Enhanced SecurityIDES Editor
The document summarizes previous work on mosaic image steganography and proposes using genetic algorithms and key-based random permutation to improve the technique. Mosaic image steganography hides a secret image by dividing it into fragments and embedding the fragments into a target image to create a mosaic. Previous methods required a large database of images or allowed only arbitrary target image selection. The proposed method uses genetic algorithms to generate a mapping sequence for embedding tile images without a database, improving clarity and reducing computational complexity. It also applies a key-based random permutation to the mapping sequence for enhanced security and robustness. The mosaic image can be recovered using the same key and mapping sequence, making it a lossless data hiding method.
Implementation of Image Steganography Using 2-Level DWT Technique .............................................1
Aayushi Verma, Rajshree Nolkha, Aishwarya Singh and Garima Jaiswal
Efficient Neighbor Routing in Wireless Mesh Networks.......................................................................1
V. Lakshmi Praba and A. Mercy Rani
Content Based Messaging Model for Library Information System........................................................1
Surbhi Agarwal, Chandrika Chanda and Senthil Murugan B.
Building an Internal Cloud for IT Support Organisations: A Preview .....................................................1
S. M. M. M Kalyan Kumar and Dr S. C. Pradhan
Use of Intelligent Business, a Method for Complete Fulfillment of E-government ................................1
M. Nili Ahmadabadi, Masoud Najafi and Peyman Gholami
Comparison of Swarm Intelligence Techniques ...................................................................................1
Prof. S. A. Thakare
An Efficient Rough Set Approach in Querying Covering Based Relational Databases.............................1
P. Prabhavathy and Dr. B. K. Tripathy
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
TEXT STEGANOGRAPHY USING LSB INSERTION METHOD ALONG WITH CHAOS THEORYIJCSEA Journal
The art of information hiding has been around nearly as long as the need for covert communication. Steganography, the concealing of information, arose early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret message within a larger image or message such that the hidden message or an image is undetectable; this is in contrast to cryptography, where the existence of the message itself is not disguised, but the content is obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a steganogram while maximizing the size of the payload. Current digital image steganography presents the challenge of hiding message in a digital image in a way that is robust to image manipulation and attack. This paper explains about how a secret message can be hidden into an image using least significant bit insertion method along with chaos.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
A novel secure image steganography method based on chaos theory in spatial do...ijsptm
This paper presents a novel approach of building a secure data hiding technique in digital images. The
image steganography technique takes the advantage of limited power of human visual system (HVS). It uses
image as cover media for embedding secret message. The most important requirement for a steganographic
algorithm is to be imperceptible while maximizing the size of the payload. In this paper a method is
proposed to encrypt the secret bits of the message based on chaos theory before embedding into the cover
image. A 3-3-2 LSB insertion method has been used for image steganography. Experimental results show a
substantial improvement in the Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF) value of the
proposed technique over the base technique of 3-3-2 LSB insertion.
An Image Steganography Algorithm Using Huffman and Interpixel Difference Enco...CSCJournals
Steganography is an art of hiding secret information on a cover medium through imperceptible methodology. The three pillars on which a steganography algorithm should be erected are: Embedding capacity, Imperceptibility and Robustness. It is fortunate that all these goals are interdependent on one another. The state of art is finding an optimum solution that keep up all the steganography goals. It is believed that there is no productivity if the size of cover medium gets extended to meet in housing the secret data on it. This happens due to lack in refinement of embedding algorithm and failing in analyzing the data structure of secret data. In this paper, an attempt has made to improve embedding capacity and bring very less distortion to the cover medium by analyzing the data structure of the payload. A residual coding is carried on the pay load before it is submitted to Huffman encoding which is a lossless compression technique. As a result, the representation of payload had shrink. Further, the variable bit encoding (Huffman) do a lossless compression and finally the payload get housed on the cover medium. This ended with high embedding capacity and less imperceptibility. Peak signal to noise ratio confirms that the residual coding had given improvised results than few existing embedding algorithm.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper on a Tree Based Parity Check (TBPC) scheme for data hiding. The TBPC scheme aims to reduce distortion when hiding data in a cover object like an image. It works by constructing a master tree from the cover object's bits and deriving a master string. The message is then hidden by XORing it with the master string to get a toggle string. A toggle tree is constructed from this and XORed with the master tree to get the stego object. The paper proposes a majority vote strategy for building the toggle tree that uses the minimum number of 1s, reducing distortion. Experimental results show the TBPC scheme effectively hides large payloads with minimal distortion.
Genetic Algorithm based Mosaic Image Steganography for Enhanced SecurityIDES Editor
The document summarizes previous work on mosaic image steganography and proposes using genetic algorithms and key-based random permutation to improve the technique. Mosaic image steganography hides a secret image by dividing it into fragments and embedding the fragments into a target image to create a mosaic. Previous methods required a large database of images or allowed only arbitrary target image selection. The proposed method uses genetic algorithms to generate a mapping sequence for embedding tile images without a database, improving clarity and reducing computational complexity. It also applies a key-based random permutation to the mapping sequence for enhanced security and robustness. The mosaic image can be recovered using the same key and mapping sequence, making it a lossless data hiding method.
Implementation of Image Steganography Using 2-Level DWT Technique .............................................1
Aayushi Verma, Rajshree Nolkha, Aishwarya Singh and Garima Jaiswal
Efficient Neighbor Routing in Wireless Mesh Networks.......................................................................1
V. Lakshmi Praba and A. Mercy Rani
Content Based Messaging Model for Library Information System........................................................1
Surbhi Agarwal, Chandrika Chanda and Senthil Murugan B.
Building an Internal Cloud for IT Support Organisations: A Preview .....................................................1
S. M. M. M Kalyan Kumar and Dr S. C. Pradhan
Use of Intelligent Business, a Method for Complete Fulfillment of E-government ................................1
M. Nili Ahmadabadi, Masoud Najafi and Peyman Gholami
Comparison of Swarm Intelligence Techniques ...................................................................................1
Prof. S. A. Thakare
An Efficient Rough Set Approach in Querying Covering Based Relational Databases.............................1
P. Prabhavathy and Dr. B. K. Tripathy
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
USING BIAS OPTIMIAZATION FOR REVERSIBLE DATA HIDING USING IMAGE INTERPOLATIONIJNSA Journal
In this paper, we propose a reversible data hiding method in the spatial domain for compressed grayscale images. The proposed method embeds secret bits into a compressed thumbnail of the original image by using a novel interpolation method and the Neighbour Mean Interpolation (NMI) technique as scaling up to the original image occurs. Experimental results presented in this paper show that the proposed method has significantly improved embedding capacities over the approach proposed by Jung and Yoo.
The document proposes a reversible data hiding method that embeds secret bits into a compressed thumbnail image during an image interpolation process. As the original thumbnail is scaled up to the original size, secret data is embedded by modifying pixel values based on their maximum and minimum neighboring pixel values in the original thumbnail. Experimental results show this method achieves higher embedding capacities than an existing approach.
Compression technique using dct fractal compressionAlexander Decker
This document summarizes and compares different image compression techniques, including DCT, fractal compression, and their applications in steganography. It discusses how DCT works by transforming image data into frequency domains, while fractal compression exploits self-similarity within images. The document reviews several existing studies on combining these techniques with steganography and encryption. Specifically, it examines approaches that use DCT and fractal compression to improve data hiding capacity and security. Overall, the document provides an overview of key compression algorithms and their applications in digital watermarking and steganography.
11.compression technique using dct fractal compressionAlexander Decker
1) The document discusses and compares different image compression techniques, specifically DCT and fractal compression.
2) Fractal compression works by finding self-similar patterns within an image during encoding, but can have a long computation time. DCT transforms an image into frequency coefficients that can be quantized for compression.
3) The document reviews previous work combining DCT and fractal compression with steganography and encryption to improve hiding capacity, imperceptibility, and security against subterfuge attacks. However, prior methods had limitations like low data hiding amounts or lack of protection for compressed data.
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
This document proposes a video data embedding scheme that hides data in an AVI video file by replacing the least significant bits (LSBs) of pixels in the video frames. It analyzes replacing 1, 2, or 3 LSBs and calculates the peak signal-to-noise ratio and correlation between the original and embedded frames. Replacing higher numbers of LSBs (3 bits vs 1 bit) decreases correlation more, improving security but also lowering visual quality. Experimental results show 3-bit LSB substitution achieves a correlation of around 0.9968, indicating greater security than 1-bit or 2-bit substitution, though at the cost of increased distortion. The scheme could securely transmit hidden information via modified video files without
4 image steganography combined with des encryption pre processingAlok Padole
This document discusses combining image steganography with DES encryption as a pre-processing step to improve security. It first encrypts secret information using DES encryption, which changes the statistical characteristics of the information. It then hides the encrypted information in an image using LSB steganography. This combined approach improves imperceptibility by reducing the distortion of the image histogram during embedding. Experimental results showed the combined approach has better anti-detection robustness than using LSB steganography alone.
37 c 551 - reduced changes in the carrier of steganography algorithmMohammed Kharma
Steganography is the science that involves
communicating secret information in an appropriate
carrier so no one apart from the sender and the recipient
even can recognize that there is hidden
information. Steganography is the art of hiding
messages inside unsuspicious medium such as images,
videos, various types of files…etc. It's a method to
establish a secure communication channel between two
parties. The purpose of steganography is to hide the
existence of a message from an eavesdropper or third
parties. Steganalysis is the branch of data processing
that seeks the identification of carrier vessels and
retrieval of message hidden. In this paper we present
enhanced implementation for Steganography algorithm,
an algorithm that we claim to be safe, built over DCT
(Discrete Cosine Transformation) frequency
domain mutation[12], the algorithm uses error reductive
measurements such as pattern matching to obtain
a reasonable a better image quality by reducing number
of changes that steganography algorithm made during
the embedding process.
A novel steganographic technique based on lsb dct approach by Mohit GoelMohit Goel
The document summarizes a research paper presented at the National Conference on Emerging Trends in Information and Computing Technologies. The paper proposes a novel steganographic technique that embeds data by altering the least significant bit of low frequency discrete cosine transform coefficients of image blocks. Experimental results showed the technique has a better peak signal-to-noise ratio value and higher data capacity compared to other techniques like least significant bit, modulus arithmetic, and SSB4-DCT embedding. It also maintains satisfactory security as the secret message cannot be extracted without knowing the decoding algorithm.
Adaptive block-based pixel value differencing steganographyOsama Hosam
Steganography is the science of hiding secure data in digital carriers such as images and videos. Pixel value differencing
(PVD) steganography algorithms embed data into images depending on pixel neighborhood differences. We have pro-
posed PVD scheme for embedding secure data into digital images. The image is divided into non-overlapping 33 blocks.
The block’s median pixel is used as a reference for calculating pixel differences. The distance between the minimum and
maximum differences are fine tuned for spreading the secure data on a wide range of image regions with high-intensity
fluctuations. The embedding procedure embeds secure data into the content regions with edges and intensity transitions.
Texture images provide higher embedding size compared with regular images. The results showed that the proposed
algorithm is successfully able to avoid smooth regions in the embedding process. In addition, the proposed algorithm
shows better embedding quality compared with the state of the art PVD approaches especially with low-embedding rates.
Information Hiding using LSB Technique based on Developed PSO Algorithm IJECEIAES
Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev. -PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most efficient and speed. An agents population is used in determining process of a required goals at search space for solving of problem. The (Dev.-PSO) algorithm is applied to different images; the number of an image which used in the experiments in this paper is three. For all used images, the Peak Signal to Noise Ratio (PSNR) value is computed. Finally, the PSNR value of the stego-A that obtained from blue sub-band colo is equal (44.87) dB, while the stego-B is equal (44.45) dB, and the PSNR value for the stego-C is (43.97)dB, while the vlue of MSE that obtained from the same color subbans is (0.00989), stego-B equal to (0.01869), and stego-C is (0.02041). Furthermore, our proposed method has ability to survive the quality for the stego image befor and after hiding stage or under intended attack that used in the existing paper such as Gaussian noise, and salt & pepper noise.
Iaetsd design of image steganography using haar dwtIaetsd Iaetsd
This document proposes a design for image steganography using Haar discrete wavelet transform (DWT) and average alpha blending techniques. The Haar DWT is used to decompose images into four frequency bands (LL, LH, HL, HH) because it requires less hardware than other transforms like DCT or DFT. The LL bands of the cover and secret images are then blended using average alpha blending according to an alpha value, which represents the percentage of pixel values considered. This blending embeds the secret image into the cover image in the frequency domain. The design aims to balance imperceptibility, quality, and capacity while reducing hardware requirements compared to other transforms.
A novel hash based least significant bit (2 3-3) image steganography in spati...ijsptm
The document presents a novel hash-based 2-3-3 least significant bit (LSB) image steganography technique for embedding secret images in the spatial domain of color cover images. The technique embeds 8 bits of secret image data at a time in the LSBs of color image pixels in a 2-3-3 pattern across the red, green, and blue channels. Experimental results show the proposed 2-3-3 technique improves mean squared error and peak signal-to-noise ratio values compared to the base 3-3-2 LSB insertion technique. The proposed technique provides better imperceptibility of the stego image and higher embedding capacity than previous hash-based LSB methods.
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COMPARISON OF SECURE AND HIGH CAPACITY COLOR IMAGE STEGANOGRAPHY TECHNIQUES I...ijait
This document compares color image steganography techniques in the RGB and YCbCr color spaces. It summarizes previous related work and then describes a proposed method that hides two grayscale images in a color image. For RGB, the secret images are hidden in the green and blue color channels by matching blocks and storing the locations in encrypted keys. For YCbCr, one secret image is hidden in the Cb channel and the other in the Cr channel in the same way. The keys are extracted during retrieval and used to reconstruct the secret images from the color channels. Experimental results show YCbCr provides better steganography than RGB in terms of security and quality of extracted secret images.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Design of an adaptive JPEG Steganalysis with UED IJCERT JOURNAL
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. The internet as a whole does not use secure links, thus information in transit may be vulnerable to interception as well. The important of reducing a chance of the information being detected during the transmission is being an issue now days. In this paper, we proposed a class of new distortion functions known as uniform embedding distortion function (UED) is presented. By incorporating the syndrome trellis coding, the best code word with undetectable data hiding is achieved. Due to hiding more amounts of data into the intersected area, embedding capacity is increased. Our aim is to hide the secret information behind the image file. Steganography hides the secret message so that intruder’s can’t detect the communication. When hiding data into the intersected area, thus provides a higher level of security with more efficient data mean square error is reduced and embedding capacity is increased.
A NOVEL APPROACH OF IMAGE STEGANOGRAPHY FOR SECRET COMMUNICATION USING SPACIN...IJNSA Journal
Steganography is the art of hiding a digital media with another digital media, it is very important to transmit a secret data from place to another because if any one intercept the data during the transmission he can't know if there is a data a data or not. This paper shows a new method to hide a secret data in an image without any bit change of the stego image that means the PSNR value between the original image and stego image equal to Infinity. The size of the secret message that can be hidden in the image is infinity or unlimited. This method based on generating a dynamic symmetric key between the sender and the receiver, it is used for encoding and decoding process and it is derived from the image and the secret message together.
This document summarizes an article that proposes a new image steganography technique using discrete wavelet transform. The technique applies an adaptive pixel pair matching method from the spatial domain to the frequency domain. Data is embedded in the middle frequencies of the discrete wavelet transformed image because they are more robust to attacks than high frequencies. The coefficients in the low frequency sub-band are preserved unchanged to improve image quality. The experimental results showed better performance with discrete wavelet transform compared to the spatial domain.
Improved LSB Steganograhy Technique for grayscale and RGB imagesIJERA Editor
A number of techniques are there to converse securely. Encryption and cryptography are enabling us to have a secure conversation. To protect privacy and communicate in an undetectable way it is required to use some steganography technique. This is to hide messages in some other media generally called cover object. In todays digital world where images are a common means of information sharing, most of the steganography techniques use digital images as a carrier for hiding message. In this paper a LSB based technique is proposed for steganograpgy. This technique is different from standard LSB technique that along with message hidden in LSB bits a part of message also resides at other selective bits using a key. The method is developed to increase the payload capacity and make detection impossible.
Stegnography of high embedding efficiency by using an extended matrix encodin...eSAT Journals
Abstract F5 Steganography is way totally different from most of LSB replacement or matching steganographic schemes, as a result of matrix encryption is used to extend embedding potency while reducing the amount of necessary changes. By victimisation this theme, the hidden message inserted into carrier media observably is transferred via a safer imperceptible channel. The embedding domain is that the quantitative DCT coefficients of JPEG image, which makes the theme, be proof against visual attack and statistical attack from the steganalyst. Based on this effective theme, An extended matrix encoding algorithm is planned to improve the performance further in this paper. The embedding potency and embedding rate get accrued to large extent by changing the hash function in matrix encryption and changing the coding mode. Eventually, the experimental results demonstrate the extended algorithm is more advanced and efficient to the classic F5 Steganography.
This document presents a new image steganography technique called M16M (Mode 16 Method). It embeds secret messages into digital images in 3 steps: 1) selecting seed pixels, 2) choosing neighboring pixels, and 3) modifying pixel intensities according to the message bits. Modifying intensities slightly allows embedding large payloads without noticeable quality loss. Future directions may combine steganography with cryptography for stronger security or use it for digital watermarking applications. Steganography can enhance security for confidential documents and will likely be important for digital watermarking and copyright protection going forward.
USING BIAS OPTIMIAZATION FOR REVERSIBLE DATA HIDING USING IMAGE INTERPOLATIONIJNSA Journal
In this paper, we propose a reversible data hiding method in the spatial domain for compressed grayscale images. The proposed method embeds secret bits into a compressed thumbnail of the original image by using a novel interpolation method and the Neighbour Mean Interpolation (NMI) technique as scaling up to the original image occurs. Experimental results presented in this paper show that the proposed method has significantly improved embedding capacities over the approach proposed by Jung and Yoo.
The document proposes a reversible data hiding method that embeds secret bits into a compressed thumbnail image during an image interpolation process. As the original thumbnail is scaled up to the original size, secret data is embedded by modifying pixel values based on their maximum and minimum neighboring pixel values in the original thumbnail. Experimental results show this method achieves higher embedding capacities than an existing approach.
Compression technique using dct fractal compressionAlexander Decker
This document summarizes and compares different image compression techniques, including DCT, fractal compression, and their applications in steganography. It discusses how DCT works by transforming image data into frequency domains, while fractal compression exploits self-similarity within images. The document reviews several existing studies on combining these techniques with steganography and encryption. Specifically, it examines approaches that use DCT and fractal compression to improve data hiding capacity and security. Overall, the document provides an overview of key compression algorithms and their applications in digital watermarking and steganography.
11.compression technique using dct fractal compressionAlexander Decker
1) The document discusses and compares different image compression techniques, specifically DCT and fractal compression.
2) Fractal compression works by finding self-similar patterns within an image during encoding, but can have a long computation time. DCT transforms an image into frequency coefficients that can be quantized for compression.
3) The document reviews previous work combining DCT and fractal compression with steganography and encryption to improve hiding capacity, imperceptibility, and security against subterfuge attacks. However, prior methods had limitations like low data hiding amounts or lack of protection for compressed data.
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
This document proposes a video data embedding scheme that hides data in an AVI video file by replacing the least significant bits (LSBs) of pixels in the video frames. It analyzes replacing 1, 2, or 3 LSBs and calculates the peak signal-to-noise ratio and correlation between the original and embedded frames. Replacing higher numbers of LSBs (3 bits vs 1 bit) decreases correlation more, improving security but also lowering visual quality. Experimental results show 3-bit LSB substitution achieves a correlation of around 0.9968, indicating greater security than 1-bit or 2-bit substitution, though at the cost of increased distortion. The scheme could securely transmit hidden information via modified video files without
4 image steganography combined with des encryption pre processingAlok Padole
This document discusses combining image steganography with DES encryption as a pre-processing step to improve security. It first encrypts secret information using DES encryption, which changes the statistical characteristics of the information. It then hides the encrypted information in an image using LSB steganography. This combined approach improves imperceptibility by reducing the distortion of the image histogram during embedding. Experimental results showed the combined approach has better anti-detection robustness than using LSB steganography alone.
37 c 551 - reduced changes in the carrier of steganography algorithmMohammed Kharma
Steganography is the science that involves
communicating secret information in an appropriate
carrier so no one apart from the sender and the recipient
even can recognize that there is hidden
information. Steganography is the art of hiding
messages inside unsuspicious medium such as images,
videos, various types of files…etc. It's a method to
establish a secure communication channel between two
parties. The purpose of steganography is to hide the
existence of a message from an eavesdropper or third
parties. Steganalysis is the branch of data processing
that seeks the identification of carrier vessels and
retrieval of message hidden. In this paper we present
enhanced implementation for Steganography algorithm,
an algorithm that we claim to be safe, built over DCT
(Discrete Cosine Transformation) frequency
domain mutation[12], the algorithm uses error reductive
measurements such as pattern matching to obtain
a reasonable a better image quality by reducing number
of changes that steganography algorithm made during
the embedding process.
A novel steganographic technique based on lsb dct approach by Mohit GoelMohit Goel
The document summarizes a research paper presented at the National Conference on Emerging Trends in Information and Computing Technologies. The paper proposes a novel steganographic technique that embeds data by altering the least significant bit of low frequency discrete cosine transform coefficients of image blocks. Experimental results showed the technique has a better peak signal-to-noise ratio value and higher data capacity compared to other techniques like least significant bit, modulus arithmetic, and SSB4-DCT embedding. It also maintains satisfactory security as the secret message cannot be extracted without knowing the decoding algorithm.
Adaptive block-based pixel value differencing steganographyOsama Hosam
Steganography is the science of hiding secure data in digital carriers such as images and videos. Pixel value differencing
(PVD) steganography algorithms embed data into images depending on pixel neighborhood differences. We have pro-
posed PVD scheme for embedding secure data into digital images. The image is divided into non-overlapping 33 blocks.
The block’s median pixel is used as a reference for calculating pixel differences. The distance between the minimum and
maximum differences are fine tuned for spreading the secure data on a wide range of image regions with high-intensity
fluctuations. The embedding procedure embeds secure data into the content regions with edges and intensity transitions.
Texture images provide higher embedding size compared with regular images. The results showed that the proposed
algorithm is successfully able to avoid smooth regions in the embedding process. In addition, the proposed algorithm
shows better embedding quality compared with the state of the art PVD approaches especially with low-embedding rates.
Information Hiding using LSB Technique based on Developed PSO Algorithm IJECEIAES
Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev. -PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most efficient and speed. An agents population is used in determining process of a required goals at search space for solving of problem. The (Dev.-PSO) algorithm is applied to different images; the number of an image which used in the experiments in this paper is three. For all used images, the Peak Signal to Noise Ratio (PSNR) value is computed. Finally, the PSNR value of the stego-A that obtained from blue sub-band colo is equal (44.87) dB, while the stego-B is equal (44.45) dB, and the PSNR value for the stego-C is (43.97)dB, while the vlue of MSE that obtained from the same color subbans is (0.00989), stego-B equal to (0.01869), and stego-C is (0.02041). Furthermore, our proposed method has ability to survive the quality for the stego image befor and after hiding stage or under intended attack that used in the existing paper such as Gaussian noise, and salt & pepper noise.
Iaetsd design of image steganography using haar dwtIaetsd Iaetsd
This document proposes a design for image steganography using Haar discrete wavelet transform (DWT) and average alpha blending techniques. The Haar DWT is used to decompose images into four frequency bands (LL, LH, HL, HH) because it requires less hardware than other transforms like DCT or DFT. The LL bands of the cover and secret images are then blended using average alpha blending according to an alpha value, which represents the percentage of pixel values considered. This blending embeds the secret image into the cover image in the frequency domain. The design aims to balance imperceptibility, quality, and capacity while reducing hardware requirements compared to other transforms.
A novel hash based least significant bit (2 3-3) image steganography in spati...ijsptm
The document presents a novel hash-based 2-3-3 least significant bit (LSB) image steganography technique for embedding secret images in the spatial domain of color cover images. The technique embeds 8 bits of secret image data at a time in the LSBs of color image pixels in a 2-3-3 pattern across the red, green, and blue channels. Experimental results show the proposed 2-3-3 technique improves mean squared error and peak signal-to-noise ratio values compared to the base 3-3-2 LSB insertion technique. The proposed technique provides better imperceptibility of the stego image and higher embedding capacity than previous hash-based LSB methods.
Steganography Using Reversible Texture Synthesis1crore projects
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COMPARISON OF SECURE AND HIGH CAPACITY COLOR IMAGE STEGANOGRAPHY TECHNIQUES I...ijait
This document compares color image steganography techniques in the RGB and YCbCr color spaces. It summarizes previous related work and then describes a proposed method that hides two grayscale images in a color image. For RGB, the secret images are hidden in the green and blue color channels by matching blocks and storing the locations in encrypted keys. For YCbCr, one secret image is hidden in the Cb channel and the other in the Cr channel in the same way. The keys are extracted during retrieval and used to reconstruct the secret images from the color channels. Experimental results show YCbCr provides better steganography than RGB in terms of security and quality of extracted secret images.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Design of an adaptive JPEG Steganalysis with UED IJCERT JOURNAL
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. The internet as a whole does not use secure links, thus information in transit may be vulnerable to interception as well. The important of reducing a chance of the information being detected during the transmission is being an issue now days. In this paper, we proposed a class of new distortion functions known as uniform embedding distortion function (UED) is presented. By incorporating the syndrome trellis coding, the best code word with undetectable data hiding is achieved. Due to hiding more amounts of data into the intersected area, embedding capacity is increased. Our aim is to hide the secret information behind the image file. Steganography hides the secret message so that intruder’s can’t detect the communication. When hiding data into the intersected area, thus provides a higher level of security with more efficient data mean square error is reduced and embedding capacity is increased.
A NOVEL APPROACH OF IMAGE STEGANOGRAPHY FOR SECRET COMMUNICATION USING SPACIN...IJNSA Journal
Steganography is the art of hiding a digital media with another digital media, it is very important to transmit a secret data from place to another because if any one intercept the data during the transmission he can't know if there is a data a data or not. This paper shows a new method to hide a secret data in an image without any bit change of the stego image that means the PSNR value between the original image and stego image equal to Infinity. The size of the secret message that can be hidden in the image is infinity or unlimited. This method based on generating a dynamic symmetric key between the sender and the receiver, it is used for encoding and decoding process and it is derived from the image and the secret message together.
This document summarizes an article that proposes a new image steganography technique using discrete wavelet transform. The technique applies an adaptive pixel pair matching method from the spatial domain to the frequency domain. Data is embedded in the middle frequencies of the discrete wavelet transformed image because they are more robust to attacks than high frequencies. The coefficients in the low frequency sub-band are preserved unchanged to improve image quality. The experimental results showed better performance with discrete wavelet transform compared to the spatial domain.
Improved LSB Steganograhy Technique for grayscale and RGB imagesIJERA Editor
A number of techniques are there to converse securely. Encryption and cryptography are enabling us to have a secure conversation. To protect privacy and communicate in an undetectable way it is required to use some steganography technique. This is to hide messages in some other media generally called cover object. In todays digital world where images are a common means of information sharing, most of the steganography techniques use digital images as a carrier for hiding message. In this paper a LSB based technique is proposed for steganograpgy. This technique is different from standard LSB technique that along with message hidden in LSB bits a part of message also resides at other selective bits using a key. The method is developed to increase the payload capacity and make detection impossible.
Stegnography of high embedding efficiency by using an extended matrix encodin...eSAT Journals
Abstract F5 Steganography is way totally different from most of LSB replacement or matching steganographic schemes, as a result of matrix encryption is used to extend embedding potency while reducing the amount of necessary changes. By victimisation this theme, the hidden message inserted into carrier media observably is transferred via a safer imperceptible channel. The embedding domain is that the quantitative DCT coefficients of JPEG image, which makes the theme, be proof against visual attack and statistical attack from the steganalyst. Based on this effective theme, An extended matrix encoding algorithm is planned to improve the performance further in this paper. The embedding potency and embedding rate get accrued to large extent by changing the hash function in matrix encryption and changing the coding mode. Eventually, the experimental results demonstrate the extended algorithm is more advanced and efficient to the classic F5 Steganography.
This document presents a new image steganography technique called M16M (Mode 16 Method). It embeds secret messages into digital images in 3 steps: 1) selecting seed pixels, 2) choosing neighboring pixels, and 3) modifying pixel intensities according to the message bits. Modifying intensities slightly allows embedding large payloads without noticeable quality loss. Future directions may combine steganography with cryptography for stronger security or use it for digital watermarking applications. Steganography can enhance security for confidential documents and will likely be important for digital watermarking and copyright protection going forward.
The Nifty future closed slightly lower by 0.05% while open interest increased. Cost of carry decreased significantly. Volatility as measured by the India VIX index increased by 1.74%. Key gainers included HCLTech, TCS, and Tata Steel while losers were ICICI Bank, LT, and BHEL based on percentage change. Put options saw additions at strike prices of 8500 and 8400 while call options saw additions at 8700 and 8800. The derivative report provided analysis of index and stock futures and options market activity and open interest levels.
El documento habla sobre diagramas de flujo. Explica que los diagramas de flujo son representaciones gráficas de algoritmos o procesos que usan símbolos definidos y flechas para mostrar el flujo de ejecución. Ofrece como ventajas que ayudan a comprender procesos, identificar mejoras, y mejorar la comunicación. También menciona que se usan para representar etapas de un proceso y sus interacciones. Incluye un ejemplo de diagrama de flujo para el uso de una cabina telefónica.
Las redes sociales en internet son aplicaciones web que favorecen el contacto entre individuos mediante la creación de perfiles, el intercambio de mensajes y la conexión con otros usuarios. Existen diversos tipos de redes sociales como Facebook, Twitter y LinkedIn, las cuales se basan en los vínculos entre sus usuarios y pueden ser genéricas, profesionales o temáticas.
The document contains quotes about education and learning. It emphasizes that education is an ongoing process of learning and changing throughout life, not something confined to schools. Education allows people to better understand the world, be more tolerant of others, and potentially change the world. The quotes encourage viewing one's education and degree not just as a ticket to a good job, but as an opportunity to have a positive impact.
The image of earth is on the hands of every individualFassil
It is useful to know there are 3 categories of 4 years in the solar year system. Category one or A is the first year that new year begins on 1 Meskeram, when September is 12 in the Gregorian calendar. Category year two or B is two years in which each year has similar beginning and ending days. This means that in category year two, each year begins on 1 Meskeram, when September is 11 and ends on Pagume 5, when September is 10. Category year three or C is the leap year that ends on Pagume 6, when September is 11. The day Pagume 6 is registered on the Ethiopian calendar, when September is 12 in the Gregorian, once in every four year. Therefore, it is the source of 3 categories 4 years. When Pagume 6 ends on a leap year, the first category year begins on 1 Meskeram, when September is 12. Category A and B are year of 365 days each, and Category C is year of 366 days or leap year. Therefore, the process of 3 by 4 cyclical years is continues.
Este documento presenta un esquema sobre el tema 1 de 2o de la ESO sobre el mantenimiento de la vida. Explica las funciones vitales de los seres vivos como la nutrición, relación y reproducción. Describe la composición química de los seres vivos y las biomoléculas inorgánicas y orgánicas. Resume la historia del descubrimiento de la célula y la teoría celular. Explica las características y estructuras de las células procariotas y eucariotas.
This document provides an overview of steganography presented by four students. It defines steganography as hiding secret communications such that others do not know of the message's existence. The document outlines the history of steganography, modern applications, types of techniques including LSB substitution and transform domains, characteristics, classifications, uses in text, images, and networks, and challenges around detection. It concludes that steganography allows covert transmission of secrets but also poses challenges for network monitoring.
DWT based approach for steganography using biometricsSri Madhur
This document discusses biometric steganography, which is a method of hiding secret data within skin regions of images. It begins by providing background on steganography and defining key terms. It then describes the specific steganography method used, which embeds secret data in the skin tone region of an image using the discrete wavelet transform after detecting skin tones via HSV color space. The document outlines the process, including carrier image, embedding in DWT sub-bands, extraction, and defines terms like PSNR. It concludes that embedding only in skin regions rather than the whole image enhances security, and cropping the image before transmission provides additional security.
The document proposes a new video watermarking algorithm using the dual-tree complex wavelet transform (DTCWT). The DTCWT offers advantages like shift invariance and directional selectivity. The algorithm embeds a watermark by adding its coefficients to high frequency DTCWT coefficients of video frames. Masks are used to hide the watermark perceptually. Experimental results show the proposed method is robust to geometric distortions, lossy compression, and a joint attack, outperforming comparable DWT-based methods. It is suitable for playback control due to its robustness and simple implementation.
Steganography is the art and science of hiding information by embedding messages within other harmless media so as not to arouse suspicion. It differs from cryptography in that the goal is to conceal the very existence of the message, not just its content. Common techniques include hiding data in the least significant bits of images, altering text formatting, and embedding signals in audio files like echoes. Detection methods involve looking for anomalies introduced by hidden data or disabling embedded data through compression or filtering. Steganography has applications in secure communication, copyright protection, and covert messaging.
A Review of Comparison Techniques of Image SteganographyIOSR Journals
This document reviews and compares three common techniques for hiding information in digital images: Least Significant Bit (LSB) steganography, Discrete Cosine Transform (DCT) steganography, and Discrete Wavelet Transform (DWT) steganography. LSB is implemented in the spatial domain by replacing the least significant bits of cover image pixels with payload bits. DCT and DWT are implemented in the frequency domain by transforming the cover image and embedding payload bits in the transformed coefficients. The document evaluates and compares the performance of these three techniques based on metrics like mean squared error, peak signal-to-noise ratio, embedding capacity, and robustness.
Modified weighted embedding method for image steganographyIAEME Publication
This document proposes a modified weighted embedding method for image steganography. It begins by discussing traditional LSB substitution methods and their weaknesses. It then describes the proposed method, which embeds data by complementing LSBs in image pixels based on the decimal value of the data, rather than direct bit replacement. This is intended to provide better security while maintaining high image quality. The embedding algorithm works by converting the data to decimal, dividing the cover image into blocks, and complementing LSBs in the block pixels based on the decimal digits and an embedding table. Extraction works similarly but in reverse. Experiments on grayscale images are said to support the method.
Developing Algorithms for Image Steganography and Increasing the Capacity Dep...IJCNCJournal
The document proposes three methods for image steganography to increase hiding capacity: 1) Modifying image edges to increase edge pixels for hiding bits. 2) Selecting lighter color regions and converting to white pixels for hiding. 3) Combining 1) and 2) using a chaotic map to randomly choose edge or region pixels. The LSB technique hides secret bits in the last 2-bits of chosen pixels. Experimental results showed the combined method achieved a PSNR of 62.72 dB, comparable to other techniques.
DEVELOPING ALGORITHMS FOR IMAGE STEGANOGRAPHY AND INCREASING THE CAPACITY DEP...IJCNCJournal
Steganography is a vital technique for transferring confidential information via an insecure network. In
addition, digital images are used as a cover to communicate sensitive information. The Least Significant
Bit (LSB) method is one of the simplest ways to insert secret data into a cover image. In this paper, the
secret text is compressed twice by an Arithmetic coding algorithm, and the resulting secret bits are hidden
in the cover pixels of the image corresponding to the pixels of each of the following three methods, one of
three methods is used in each experiment: The first method, the edges of the image are modified to increase
the number of edges, in the second method the lighter-colored regions are selected, and in the third
method, the two methods are combined together to increase security and keep the secret message
unrecognized. Hiding in each of the previous methods is done by using the LSB technique in the last 2-bit.
The correction approach is used to increase the stego image's imperceptibility. The experimental results
show that with an average message size of 29.8 kb, the average Peak Signal-to-Noise Ratio (PSNR) for the
second proposed (Light regions) method equals 62.76 dB and for the third proposed (Edge and region)
method equals 62.72 dB, which is a reasonable result when compared to other steganographic techniques.
Implementation of LSB-Based Image Steganography Method for effectiveness of D...ijsrd.com
Increased use of electronic communication has given birth to new ways of transmitting information securely. Steganography is a science of hiding information by embedding it in some other data called host message. Images are most known objects for steganography. The host message before steganography and stego message after steganography have the same characteristics. The given work is to be done by evaluating it on MATALAB. While evaluation one can calculate SNR, PSNR and BER for individual information Bit for conceal bit and analysis effect on results.
A SECURE COLOR IMAGE STEGANOGRAPHY IN TRANSFORM DOMAINijcisjournal
Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
A SECURE COLOR IMAGE STEGANOGRAPHY IN TRANSFORM DOMAINijcisjournal
Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
Stegnography of high embedding efficiency by using an extended matrix encodin...eSAT Publishing House
This document summarizes an extended matrix encoding algorithm for steganography proposed in a research paper. The algorithm aims to improve the embedding efficiency and rate of the classic F5 steganography system. It does this by extending the hash function used in matrix encoding to multiple layers, allowing more secret bits to be embedded into each carrier cell while still only modifying one bit. The encoding is represented by a quad (dmax, n, k, L) where L indicates the maximum extension layer. Secret bits are tested against specific extended codes up to layer L, and if they match, additional bits can be embedded into the carrier cell. Experimental results showed the extended algorithm performs better than the classic F5 system.
The document summarizes an adaptive image steganography technique that embeds secret messages into digital images. It proposes using adaptive quantization embedding, where quantization steps for image blocks are optimized to guarantee more data can be embedded in busy image areas with high contrast. The technique embeds adaptive quantization parameters and message bits into the cover image using a difference expanding algorithm. Simulation results showed the proposed scheme can provide a good balance between imperceptibility and embedding capacity.
This document proposes an efficient data steganography method called Adaptive Pixel Pair Matching (APPM) with high security. APPM hides data by substituting pixel pairs in a cover image based on a secret key. It defines an extraction function and compact neighborhood set for pixel pairs to minimize embedding distortion. APPM converts the secret message into digits of a B-ary numerical system for hiding. It calculates the optimal value of B and neighborhood set based on the image and message size. APPM generates a random embedding sequence using a key for substitution. It also provides an external password for additional security of the hidden message. The document claims this method provides better image quality and higher payload than previous pixel pair matching methods with increased security.
A Survey of different Data Hiding Techniques in Digital Imagesijsrd.com
Steganography is the art and science of invisible communication, which hides the existence of the communicated message into media such as text, audio, image and video without any suspicion. Steganography is different from cryptography and watermarking in its objectives which includes undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data. Image Steganography uses digital image as its cover media. This paper analyzes and discusses various techniques available today for image steganography along with their strengths and weaknesses.
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
An Image representation using Compressive Sensing and Arithmetic Coding IJCERT
The demand for graphics and multimedia communication over intenet is growing day by day. Generally the coding efficiency achieved by CS measurements is below the widely used wavelet coding schemes (e.g., JPEG 2000). In the existing wavelet-based CS schemes, DWT is mainly applied for sparse representation and the correlation of DWT coefficients has not been fully exploited yet. To improve the coding efficiency, the statistics of DWT coefficients has been investigated. A novel CS-based image representation scheme has been proposed by considering the intra- and inter-similarity among DWT coefficients. Multi-scale DWT is first applied. The low- and high-frequency subbands of Multi-scale DWT are coded separately due to the fact that scaling coefficients capture most of the image energy. At the decoder side, two different recovery algorithms have been presented to exploit the correlation of scaling and wavelet coefficients well. In essence, the proposed CS-based coding method can be viewed as a hybrid compressed sensing schemes which gives better coding efficiency compared to other CS based coding methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Secure Color Image Steganography in Transform Domain ijcisjournal
Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in
the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an
adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a
learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image. The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth
safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are
discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
IMPROVED STEGANOGRAPHIC SECURITY BY APPLYING AN IRREGULAR IMAGE SEGMENTATION ...IJNSA Journal
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image.
The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
Reference-free differential histogram-correlative detection of steganography:...nooriasukmaningtyas
Recent research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless modified.
AN ADAPTIVE PSEUDORANDOM STEGO-CRYPTO TECHNIQUE FOR DATA COMMUNICATIONIJCNCJournal
The document describes a proposed adaptive pseudorandom stego-crypto technique for data communication. The technique combines stream cipher cryptography with a modified pseudorandom LSB substitution technique. This provides an evenly distributed cipher text while also enhancing security through increased brute force search times and reduced time complexity by avoiding collisions during random pixel selection. The proposed method uses three parameters that are optimized through experimental analysis to minimize distortions, increase cipher text scattering, and reduce collisions and time complexity. Results demonstrate the technique maintains good perceptual quality while improving upon previous methods.
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
A Spatial Domain Image Steganography Technique Based on Matrix Embedding and Huffman Encoding
1. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 456
A Spatial Domain Image Steganography Technique Based on
Matrix Embedding and Huffman Encoding
P.Nithyanandam nithyanandamp@ssn.edu.in
Department of Computer Application
SSN College of Engineering,
Anna University of Technology, Chennai
Kanchipuram Dt, Tamilnadu , 603110,India
T.Ravichandran dr.t.ravichandran@gmail.com
Principal
Hindustan Institute of Technology,
Anna University of Technology, Coimbatore
Coimbatore Dt,Tamilnadu, 641032,India
N.M.Santron nmsantron@gmail.com
III Year M.C.A.
Department of Computer Application
SSN College of Engineering,
Anna University of Technology, Chennai
Kanchipuram Dt, Tamilnadu , 603110,India
E.Priyadharshini indrapriyadharshini.e@gmail.com
III Year M.C.A.
Department of Computer Application
SSN College of Engineering,
Anna University of Technology, Chennai
Kanchipuram Dt, Tamilnadu , 603110,India
Abstract
This paper presents an algorithm in spatial domain which gives less distortion to the cover image
during embedding process. Minimizing embedding impact and maximizing embedding capacity
are the key factors of any steganography algorithm. Peak Signal to Noise Ratio (PSNR) is the
familiar metric used in discriminating the distorted image (stego image) and cover image. Here
matrix embedding technique is chosen to embed the secret image which is initially Huffman
encoded. The Huffman encoded image is overlaid on the selected bits of all the channels of
pixels of cover image through matrix embedding. As a result, the stego image is constructed with
very less distortion when compared to the cover image ends up with higher PSNR value. A secret
image which cannot be embedded in a normal LSB embedding technique can be overlaid in this
proposed technique since the secret image is Huffman encoded. Experimental results for
standard cover images, which obtained higher PSNR value during the operation is shown in this
paper.
Keywords: Steganography, Imperceptibility, Payload, Stego Image, Least Significant Bit (LSB),
Huffman Encoding, Matrix Embedding, Peak Signal to Noise Ratio (PSNR), Mean Square Error
(MSE) and Discrete Wavelet Transformation (DWT).
1. INTRODUCTION
Steganography is the art of secret communication. It has apparent difference with cryptography;
because cryptography hides information content whereas steganography hides information
existence. Steganography is broadly classified in to spatial and frequency domain technique.
Least Significant Bit (LSB) replacement, LSB matching, Matrix embedding and Pixel value
2. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 457
differencing are some of the spatial domain techniques. Frequency domain techniques include
Outguess, F5, JP Hide and Seek. Fundamentally, a steganography algorithm or embedding
function can influence the cover work in three different ways, namely cover lookup, cover
synthesis and cover modification. Naturally, changes of larger scale will be more obvious than
changes of smaller scale. As a result, most steganographic schemes try to minimize the distortion
on cover work. The location of changes is controlled by the selection rule [1]. There are three
types of rule namely sequential, random and adaptive.
The primary goal of steganography is to design embedding function that should be statistically
undetectable and capable of communicating large payloads. There exists a tradeoff between
embedding capacity and proportion of distortion. There are many algorithms evolving to
accomplish steganography goal in both spatial and frequency domain. Minimizing the embedding
impact while constructing a stego image could be one of the ways; this may thwart in applying
statistical analysis over a stego image. The notion of this paper is to apply one such embedding
technique and to produce a less distorted cover image. Supporting a higher payload on a cover
image depends upon embedding technique; but it also can be viewed in another direction of
compressing the payload before overlaying. A lossless Huffman [2] [3] [4] [5] compression prior to
overlaying results in fewer distortion in the cover image.
Cachin’s [1] description of steganography security calls for the Kullback-Leibler distance which
says, the probability distance between the cover and stego work to be as little as possible. In our
technique it is achieved by minimizing the distortion between the cover and stego work. This will
make it harder for the warden to detect embedding. The embedding procedure can encode the
message bits in many ways. For example in LSB embedding the LSB is replaced to match the
secret message bits. On average, one can embed, 2 bits per embedding change. It can be
substantially improved if we adopt a clever embedding scheme. In particular, if the payload is
shorter than the embedding capacity, one can influence the location of changes to encode more
bits per change. Let us take a look at the following simple example. Say, we have a group of
three pixels with gray scale values x1, x2 and x3. We wish to embed 2 message bits, b1 and b2.
It seems that a practical approach might be to simply replace b1 with x1 and b2 with x2 (i.e.)
replacing the LSB of the pixels to match the corresponding message bits. Assuming the 2 bits are
0 or 1 with equal probability, the expected number of changes to the whole group of pixels to
embed both bits is 1. Therefore, we embed at embedding efficiency of 2 or 2 bits per change.
However, it can be improved. Let us encode b1 = LSB (x1) XOR LSB (x2) and b2 = LSB (x2)
XOR LSB (x3). If the values of the cover work satisfy both equations with equality, no embedding
changes are required. If the first one is satisfied but not the second one, simply flip the LSB of x3.
If the second one is satisfied but not the first one, flip the LSB of x1. If neither one is satisfied, flip
LSB of x2. Because all four cases are equally likely with probability 1/4, the expected number of
changes is 3/4, which is less than what we had earlier. This embedding technique is called matrix
embedding [1] which is further extended and used in the proposed method.
Huffman compression is a variable length coding whose performance depends on the input
image bit stream. The compression is directly proportional to smoothness of the image. Higher
the smoothness and higher the redundancy will give good compression. Subjective and objective
measures [6] are the two techniques existing to test the distortion of the processed image.
Subjective measure is not reliable because human vision is a metric in assessing the distortion of
the stego objects. Human vision may vary from person to person; hence this approach is not
suitable. In objective measure, the mean square error (MSE) represents the cumulative squared
error between the stego image and cover image. A lower figure of MSE conveys lower error/
distortion between the cover and stego image.
3. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 458
∑∑− −
−=
m
i
n
j
ijij BA
nm
MSE
1 1
2
)(
*
1
The equation of MSE to assess the stego and cover object is given by:
...........[1]
Whereas Aij represents pixel in the cover image and Bij represents pixel in the stego image; m, n
represents the height and width of the image respectively. It is measured in constant and the unit
is decibel (dB).
Peak Signal to Noise Ratio (PSNR) is a metric which calculate the distortion in decibels, between
two images. Higher the PSNR indicates a better reconstructed or stego image. The PSNR is
represented by the following equation:
MSE
Max
PSNR
2
10
)(
log*10= ...………..[2]
Where max denote maximum intensity of grayscale (255).PSNR is measured in decibels (dB).
2. RELATED WORK
Chang, C.C et al., [7] has proposed an image steganography technique which offer high
embedding capacity and bring less distortion to the stego image. The embedding process embed
bits of secret bit stream on the stego image pixels. Instead of replacing the LSB of every pixel,
this method replaces the pixel intensity with similar value. The range of modifiable pixel value is
higher in edge areas than smooth areas to maintain good perceptual excellence. Various bit
embedding methods are followed; which are decided by the correlation between the actual pixel
and the neighboring pixels. The neighboring pixels may be a pixel left, right, top or bottom to the
actual pixels. The different schemes are two sided, three sided and four sided one. Two sided
scheme take upper and left pixels, three side scheme take upper, left and right whereas four
sided take upper, left, and right and bottom pixels. The embedding capacity and PSNR are
inversely proportional to the sides taken into account.
Po-Yueh Chen et al., [8] proposed an image steganography scheme which fixes the limitation of
steganography technique proposed in [7]. The limitation of [7] is falling of boundary problem
which means the pixel which is located for embedding will become unused; since it exceeds the
maximum intensity level which is greater than 255 (maximum gray scale intensity). Fewer bits are
added even on such pixels which improve the embedding capacity without compromising PSNR
in this technique.
A. Nag et al., [9] proposed a stenographic technique which is based on wavelet transformation on
the images. Discrete Wavelet Transformation (DWT) converts the spatial domain of cover image
into frequency domain. Huffman compression is applied for the stream of secret bits before
overlaying them on the cover image. A high PSNR and very high embedding capacity is
achieved.
R.Amirtharajan et al., [10] proposed a stenographic technique which is based on LSB
replacement technique. Varying lengths of secret bits get embedded in every pixel. In method1
green and blue are embedding channels keeping red channel as indicator channel. In method2
an option is provided for choosing the indicator channel among the three channels. Once chosen,
the remaining two channel act as embedding channel. In method3 the indicator channel is chosen
by rotation scheme across all the pixels. In the first pixel red channel is indicator; green channel is
the indicator in second pixel and in third channel blue act as indicator. Once indicator is finalized
the remaining two channels will be used for embedding. This scheme is repeated for the
consecutive pixels. The MSE and PSNR is calculated for all channel and the average number of
bits get embedded in every pixel is shown in their results.
4. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 459
The rest of the paper is organized as follows. Section III discusses the proposed steganography
technique. In Section IV experimental results are exhibited and discussed. Finally the conclusion
and future direction are provided for the proposed work.
3. PROPOSED METHOD
3.1. System Architecture
Fig.1 shows the overall system architecture on which the proposed study stands on. The secret
image pixel values are Huffman compressed which comprises of Huffman encodings and
Huffman table. The size of Huffman table and Huffman encodings are measured in a 32 bit
quantity each. These 64 bits are recorded across the last 64 byte’s LSB of the stego image. Both
the Huffman encodings and Huffman table binary content are embedded in the LSB of every byte
using LSB replacement or Matrix embedding technique. The binary content of Huffman table is
followed by Huffman encodings. The starting and the ending point of the corresponding binary
component i.e. Huffman encodings or Huffman table is identified through the processed individual
32 bits entry stored at the end of the stego image. In the case of the secret image being
sufficiently large, the stego image LSB may be fully utilized. Always, the last 64 byte is reserved
for storing the size of Huffman table and Huffman encodings.
FIGURE 1: Stego Image Architecture
3.2. Huffman Compression on Image
The intensity of the pixels across the image is spatially correlated [3]. Information is pointlessly
repeated in the representation of the correlated pixels. These repetitive pixels should also be
represented by fixed number of bits in unencoded Huffman format. Actually these values are the
best source for exploiting compression. A very frequent occurrence intensity value can be
represented by variable numbers of bits (i.e. shorter bits) in contrast to the fixed number of bits for
representing the pixel intensity used in unencoded Huffman technique. This is the core concept of
Huffman encoding technique. The secret image is Huffman encoded prior to embedding process.
3.3. Extended Matrix Embedding
An extended matrix embedding technique is used in proposed method. Generally (1, n, k) matrix
embedding [11] mechanism is used; which denotes k secret bits are embedded in n cover bits
with at most 1 change. Here using three Least Significant Bits of RGB channel 2 bits of secret
bits might be embedded with at most one change, which is typically (1,3,2) in the above case.
Here n is 2k
-1.
It can be further expanded by considering; more secret bits can be embedded in a single go with
at most 1 change. For example if k is 3, then n is 2
k
-1. K secret bit should be embedded in 2
k
-1
LSB of every byte: Huffman Encodings embedded
LSB of every byte: Huffman Table embedded
Non modified part: may be utilized in the case of
secret image size is large
32- bit (length of
Huffman table)
32- bit (length of
Huffman
encodings)
5. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 460
cover bit with at most 1 change. It is denoted by (1,7,3), where 1 represent number of changes
allowed,7 represent number of cover bit involved in the operation and 3 represent number of
secret bit to be embedded. Now the cover bit selection and embedding mechanism to be
designed in such a way that, k secret bits should be embedded in n cover bits with at most 1
change.
1) Cover bit Selection: Two types of cover bit selection are attempted in the above proposed
technique and the results are shown for both the types.
Method1: In this method the LSB of every byte is chosen as cover bit. 7 bits of data are required
to embed a 3 bit secret data. Those 7 bits are collected from seven consecutive bytes of the
image. All 7 bytes’ LSB is serving as cover bit.
Method2: In this method to collect 7 cover bit for the operation, on every pixel last two bits of red
channel, last three bits of green channel and last two bits of blue channel are taken.
2) Secret bit Embedding: In order to embed and extract the 3 secret bit in the 7 cover bit with
atmost 1 change, a reversible embedding and extraction algorithm should be designed. Equation
3 shown below will be used to meet the above goal. Assume b1,b2,b3 are the secret bits,
x1,x2,x3,x4,x5,x6,x7 are cover bits. The cover bits are adjusted according to the secret bits b1, b2
and b3 with atmost 1 change i.e. only one change is permitted out of all the 7 cover bits. At the
same time the secret bit should be mapped inside the cover bit. The following equation is used in
both embedding and extraction process.
. ....................[3]
The above 3 expression in equation 3 is operated to check the coincidence of secret bit against
cover bit. An exclusive OR operation is performed on the cover bit; if all the three expression is
satisfied no adjustment is required on the cover bit. Sometimes the cover bit by itself, is suitable
to fit the secret data. If any or more than one of the expressions in equation 3 is not satisfied then
modification on the cover bit is followed according to Table 1. This slight modification on the cover
bit enable the secret bit to be mapped on the cover bit with at most only one change. Since the
cover bit are adjusted according to the secret bit; during extraction the same equation can be
used in recovering the secret bit from the cover bit.
Secret
bit
Positions
not
matched
(b1,b2,b3)
1 2 3 1,2 2,3 1,3 1,2,3
Cover bit
to be
inverted
x1 x2 x3 x4 x5 x6 x7
TABLE 1: Embedding/ Extraction Reference
76533
75422
76411
xxxxb
xxxxb
xxxxb
⊕⊕⊕=
⊕⊕⊕=
⊕⊕⊕=
6. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 461
Huffman coding technique is used in the proposed method to securely and imperceptibly hide the
secret image in the cover image. The Huffman encoded bit stream and Huffman table bit stream
is embedded in the cover image pixel either by method1 or method2 through Matrix embedding
technique. Cover bit selection will differ in method1 and method2 whereas embedding process
remain same.
3.4. Hashing
Finally to attain the integrity of the stego image; the secret image is hashed prior to embedding.
This hash code should be send as a supplementary component in addition to stego image. In the
receiving end, the retrieved secret image is hashed to cross check against the hash code
received. If both the hash codes are same, it conveys no intruder has modified the stego image.
3.5. Embedding Process
Fig. 2a shows the embedding process carried on the sender side. The Hash code of secret image
and stego images are sent to receiver.
The steps carried on the sender side are given below:
Step 1: Hash the secret image.
Step 2: The Secret image is converted into a Numerical matrix which contains the RGB value or
intensity of each pixel.
Step 3: Apply Huffman encoding for the output obtained from Step 2 which results in Huffman
table and Huffman encoded secret image bit streams.
Step 4: Group the above obtained binary bit stream (Huffman table and Huffman encoded) in
chunk of three bits.
Step 5: M1: Method1:- Each color image pixel is represented by 3 bytes (RGB). Collect 7
consecutive bytes from the image. All 7 bytes’ LSB is serving as cover bit.
Step 6: M1: Method1:- Using equation 3 adjusts the 7 bytes LSB to match the three secret bit
chunk obtained in Step 4. (OR)
Step 5: M2: Method2:- Each color image pixel is represented by 3 bytes (RGB). In this method
to collect 7 cover bit for the operation, on every pixel LSB and LSB -1 from Red
channel, LSB, LSB -1 and LSB -2 from Green channel, LSB and LSB -1 from Blue
channel; a total of 7 bits are chosen as cover bit.
Step 6: M2: Method2:- Using equation 3 adjusts the above 7 bits to match the three bit chunk
obtained in Step 4.
Step 7: Repeat Step5 and Step6 until all the 3 secret bit chunks are mapped over the cover
image pixels moving from left to right and top to bottom of the cover image.
Step 8: Send the Hash Code and stego image obtained from Step 7 to the receiver.
3.6. Extraction Process
Fig. 2b shows the extraction process carried on the receiver side. Upon receiving the stego
image, and the Hash code, receiver should extract the Huffman table, Huffman encoded bit
streams, and secret image dimension from the stego image.
The steps carried on the receiver side are given below:
Step 1: Apply the relevant bit collection on the stego image pixel depends on the method
(method1/method2); the secret bit is embedded in the cover image as explained in
embedding process.
Step 2: Size of secret image, Huffman Table and Huffman symbols are retrieved.
Step 3: The Binary Huffman table is then converted to the actual format that can be accepted
by the Huffman decoding.
Step 4: The Huffman table and Huffman encodings obtained in Step 2 are used in Huffman
decoding process. As a result RGB/intensity value, for every pixel of secret image is
obtained.
Step 5: Finally, the image is constructed using all the pixels which is computed in Step 4 will
reveal the secret image.
Step 6: To ensure the stego image integrity, the received hash code is compared against the
Hash code of constructed secret image. If both are equal, cover image is free from
7. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 462
FIGURE 2a: Embedding Process
Binary
Conversion
Cover Image
Huffman
Encoding
Secret Image
Huffman
Encoded
Data
Huffman
Table
Matrix
embedding
Stego Image
Hashing
Hash file
intruder attack.
The intermediate results obtained in every stage of embedding and extraction process are
redirected to a text file may be assumed for better understanding of the proposed method
wherever required.
4. EXPERIMENTAL RESULTS
Java 2.0 and MATLAB 7.6 are the programming tools used to implement the proposed method.
PSNR, Embedding Capacity and Mean Square Error are the three metrics taken here to
consolidate the strength of proposed method. PSNR result is shown separately for all the
channels. Two tables are used to present the performance of both the methods. The same cover
image of size 256 X 256 is used in both the methods. The cover image and secret image taken
here for experimentation is 24 bit color depth bmp (Bit Map Format) image.
A secret image Cameraman Fig. 3 of various sizes is embedded in the RGB cover images like
Lena, Airplane, Baboon and Boat each of size 256 x 256. Fig. 4-7 shows the cover images,
obtained stego images and histogram arrived in method1 and method2 of matrix embedding
technique. Table2 and Table3 show the experimental results of method1 and method2
respectively. The PSNR and MSE arrived using the proposed method shows that the distortion
occurred in stego image are very less. In method1 secret image of different sizes such as 85x85,
90x90 and 95x95 with 24 bit depth are embedded. The maximum capacity that the cover image
can hold is 216,600 bits which is 26.5KB. The embedding capacity is 14% of the cover image
using method1. The average PSNR and mean in method1 for 95x95 secret image is 58 and 0.12
respectively.
In method2, since the 7 cover bits are collected on a single pixel, the embedding capacity of the
same cover image is better than method1. In method2, the same secret image Cameraman Fig. 3
of different size such as 85x85, 90x90, 95x95, 140x140, 150x150, and 155x155. In method2 a
higher capacity is achieved but PSNR and mean is compromised. The maximum capacity that the
Stego Image
Matrix extraction
FIGURE 2b: Extraction Process
Huffman encoded
binary Stream
Huffman Table
Huffman
Decoding
Size of secret
message and
Huffman Table
Secret Image
Decimal
Conversion
Hash file
Hashing Hash file
C
O
M
P
A
R
E
8. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 463
cover image can hold is 576,600 bits which is 70.38KB. The embedding capacity is 37% of the
cover image using method2. The average PSNR and mean in method2 for 155x155 secret image
is 50 and 0.6 respectively. The PSNR and mean has declined with an enhanced capacity; but still
PSNR value with more than 40 is acceptable.
Cover Image of
size 256 X 256
Red
Channel
Green
Channel
Blue
Channel
PSNR MSE PSNR MSE PSNR MSE
Lena
85 x 85 57.94 0.1044 57.90 0.1052 57.89 0.1057
90 x 90 57.63 0.1120 57.45 0.1169 57.51 0.1151
95x 95 57.18 0.1243 57.22 0.1232 57.11 0.1263
Airplane
85 x 85 57.94 0.1044 57.89 0.1057 57.82 0.1072
90 x 90 57.51 0.1151 57.61 0.1125 57.46 0.1164
95x 95 57.23 0.1227 57.12 0.1259 57.19 0.1242
Baboon
85 x 85 57.87 0.1061 57.93 0.1046 57.87 0.1060
90 x 90 57.54 0.1145 57.55 0.1141 57.49 0.1156
95x 95 57.15 0.1252 57.22 0.1232 57.17 0.1246
Boat
85 x 85 57.95 0.1040 57.88 0.1059 57.82 0.1073
90 x 90 57.56 0.1139 57.55 0.1141 57.46 0.1167
95x 95 57.15 0.1251 57.14 0.1254 57.14 0.1255
TABLE 2: 7 COVER BIT ON 7 BYTE (METHOD 1)
Cover Image of
size 256 X 256
Red
Channel
Green
Channel
Blue
Channel
PSNR MSE PSNR MSE PSNR MSE
Lena
85 x 85 54.55 0.2277 48.40 0.9393 54.55 0.2278
90 x 90 54.15 0.2497 47.96 1.0397 54.30 0.2411
95x 95 53.81 0.2700 47.72 1.0969 53.87 0.2665
140x140 51.02 0.5131 44.77 2.1646 50.97 0.5200
150x150 50.50 0.5783 44.17 2.4874 50.37 0.5959
155x155 50.25 0.6131 43.96 2.6100 50.22 0.6169
Airplane
85 x 85 54.56 0.2275 48.40 0.9379 54.57 0.2266
90 x 90 54.28 0.2423 47.94 1.0445 54.20 0.2469
95x 95 53.81 0.2700 47.57 1.1366 53.89 0.2652
140x140 50.98 0.5180 44.80 2.1505 51.02 0.5134
150x150 50.45 0.5856 44.16 2.4921 50.51 0.5781
155x155 50.23 0.6155 44.04 2.5631 50.15 0.6275
Baboon
85 x 85 54.57 0.2267 48.34 0.9527 54.64 0.2229
90 x 90 54.33 0.2397 47.97 1.0374 54.12 0.2517
95x 95 53.89 0.2652 47.70 1.1028 53.75 0.2740
140x140 51.01 0.5145 44.73 2.1881 50.97 0.5190
150x150 50.47 0.5824 44.24 2.4460 50.43 0.5882
155x155 50.21 0.6191 43.98 2.6004 50.16 0.6266
Boat
85 x 85 54.61 0.2248 48.27 0.9673 54.56 0.2272
90 x 90 54.24 0.2448 47.89 1.0569 54.27 0.2427
95x 95 53.86 0.2673 47.72 1.0984 53.81 0.2699
140x140 51.04 0.5110 44.76 2.1710 51.01 0.5147
150x150 50.43 0.5882 44.25 2.4384 50.48 0.5815
155x155 50.30 0.6064 43.93 2.6298 50.23 0.6160
TABLE 3: 7 COVER BIT ON 1 PIXEL – 2, 3,2 (METHOD 2 )
The proposed method’s hiding capacity depends upon the Huffman encoding output. The
Huffman encoded result of a secret image (Huffman encoded bit stream and Huffman Table) size
should be lesser than the total number of LSB spot available in the cover image. The last 64 pixel
9. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 464
in cover image is reserved for storing the technical details which will be used in the receiver side
to extract the secret image from the stego image. This 64 pixel (64x3=192 bytes) should be
excluded while computing the hiding capacity of cover image. Image of any size/richness can be
hidden through our proposed method, provided it meets the above said condition. Integrity of the
stego image is verified by crosschecking the hash code received against the constructed secret
image hash code. If both hash code are same, it conveys no intruder modified the stego image.
4.1. Discussion
In method2 the PSNR of green channel is less, compared to the other two channels. It is due to
the reason that the cover bits are selected in the same pixel in this order (2, 3, and 2). Two bits
from red channel, three bits from green channel and two bits from blue channel are taken. Out of
7 bits, 3 bits are taken from green channel; hence this channel is highly vulnerable to distortion.
So, as a result the PSNR of green channel has declined in method2.
We quite often found that a secret image which is richer and whose dimension is lesser than
Cameraman,(shown in Fig. 3) say 100 X 100 cannot be embedded in this 256 X 256 cover image
shown in figure 4. In contrast, a secret image which is not richer and whose dimension is higher
than 100 X 100 can be embedded in the cover image. This makes us to finalize that the
embedding capacity of our proposed technique depends on Huffman encoding. Any image,
whose Huffman compression is less, fits in the cover image irrespective of its size and richness.
The embedding capacity of the cover image can be improved further, if a pixel adjustment
process technique is adapted. The number of bits get embedded in the proposed technique is just
3 bit per pixel in method1 or 3 bit using LSB’s of seven consecutive bytes in method2. Pixel
adjustment process technique is just substituting the intensity of the every cover pixel with an
equivalent resembling pixels. This could exploit the cover pixels in embedding greater than 3 bits
(9 bits/pixel). But, it will be on the cost of compromising a little bit distortion gets introduced on the
cover image.
To discuss on security side, the proposed technique is robust enough; because extracting a data
without knowing the architecture of the proposed technique is difficult, moreover data is Huffman
encoded. Stego image integrity is validated through hashing which give confidence to the
receiver. Thus, the privacy and security issues are addressed in this proposed technique to a
reasonable extent.
CONCLUSION
We had proposed an image steganography algorithm which brings a better PSNR and MSE. The
experimental results show that distortion between cover and stego image is minimum. Capacity
improvement and distortion reduction has been addressed in this proposed technique. In the
proposed method, the embedding capacity of the cover image is increased which results in slight
decline in both PSNR and MSE parameters. The veracity of the stego image is verified and then
progressed for their usage on receiver side. The proposed technique is not robust against any
geometrical distortion such as rotation, translation, scaling, cropping etc., induced on the stego
image. Improving this parameter is still under research and not matured yet.
FUTURE WORK
The proposed algorithm should be customized to support embedding in the frequency domain. It
should be enhanced to withstand geometrical distortion induced on the image.
10. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 465
FIGURE 3: Cameraman
FIGURE 4a: Lena Cover FIGURE 4b: Red Channel FIGURE 4d: Blue ChannelFIGURE 4c: Green Channel
FIGURE 4e: Lena Stego M1 FIGURE 4f: Red Channel FIGURE 4h: Blue ChannelFIGURE 4g: Green Channel
FIGURE 4l: Blue ChannelFIGURE 4i: Lena Stego M2 FIGURE 4j: Red Channel FIGURE 4k: Green Channel
11. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 466
FIGURE 5c: Green ChannelFIGURE 5a: Airplane Cover FIGURE 5b: Red Channel FIGURE 5d: Blue Channel
FIGURE 5i: Airplane Stego M2 FIGURE 5j: Red Channel FIGURE 5l: Blue ChannelFIGURE 5k: Green Channel
FIGURE 5h: Blue ChannelFIGURE 5e: Airplane Stego M1 FIGURE 5f: Red Channel FIGURE 5g: Green Channel
FIGURE 6a: Baboon Cover FIGURE 6b: Red Channel FIGURE 6d: Blue ChannelFIGURE 6c: Green Channel
12. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 467
FIGURE 6e: Baboon Stego M1 FIGURE 6f: Red Channel FIGURE 6h: Blue ChannelFIGURE 6g: Green Channel
FIGURE 6i: Baboon Stego M2 FIGURE 6j: Red Channel FIGURE 6l: Blue ChannelFIGURE 6k: Green Channel
FIGURE 7d: Blue ChannelFIGURE 7a: Boat Cover FIGURE 7b: Red Channel FIGURE 7c: Green Channel
FIGURE 7h: Blue ChannelFIGURE 7e: Boat Stego M1 FIGURE 7f: Red Channel FIGURE 7g: Green Channel
FIGURE 7l: Blue ChannelFIGURE 7i: Boat Stego M2 FIGURE 7j: Red Channel FIGURE 7k: Green Channel
13. P.Nithyanandam, T.Ravichandran, N.M.Santron & E.Priyadarshini
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (5) : 2011 468
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