Reversible data embedding is a technique that embeds data into an image in a reversible manner. An important aspect of reversible data embedding is to find embedding area in the image and to embed the data into it. In the conventional reversible techniques, the visual quality is not taken into account which resulted in a poor quality of the embedded images. Hence the histogram modification based reversible data hiding technique using multiple causal windows is proposed which predicts the embedding level with the help of the pixel value, edge value, Just Noticeable Difference(JND) value. Using this data embedding level the data is embedded into the pixels. The pixel level adjustment considering the Human Visual System (HVS) characteristics is also made to reduce the distortion caused by data embedding. This significantly improves the data embedding capacity along with greater visual quality. The proposed method includes three phases: (i).Construction of casual window and calculation of edge and JND values in which the casual window determines the pixel values, the edge and the JND values are calculated (ii).Data embedding which is the process of embedding the data into the original image (iii). Data extractor and image recovery where the original image is recovered and the embedded bits are obtained. The experimental results and performance comparison with other reversible data hiding algorithms are presented to demonstrate the validity of the proposed algorithm. The experimental results show that the Performance of the proposed system on an average shows an accuracy of 95%.
Security and imperceptibility improving of image steganography using pixel al...IJECEIAES
Information security is one of the main aspects of processes and methodologies in the technical age of information and communication. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography and cryptography. An effective digital image-steganographic method based on odd/even pixel allocation and random function to increase the security and imperceptibility has been improved. This lately developed outline has been verified for increasing the security and imperceptibility to determine the existent problems. Huffman coding has been used to modify secret data prior embedding stage; this modified equivalent secret data that prevent the secret data from attackers to increase the secret data capacities. The main objective of our scheme is to boost the peak-signal-to-noise-ratio (PSNR) of the stego cover and stop against any attack. The size of the secret data also increases. The results confirm good PSNR values in addition of these findings confirmed the proposed method eligibility.
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.
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.
Reversible Encrypytion and Information ConcealmentIJERA Editor
Recently, a lot of attention is paid to reversible data hiding (RDH) in encrypted pictures, since it maintains the wonderful property that the initial image cover will be losslessly recovered when embedded data is extracted, whereas protects the image content that is need to be kept confidential. Other techniques used antecedently are to embed data by reversibly vacating area from the pictures, that area unit been encryted, may cause some errors on information extraction or image restoration. In this paper, we propose a unique methodology by reserving room before secret writing (i.e reserving room before encryption) with a conventional RDH algorithmic rule, and thus it becomes straightforward for hider to reversibly embed data in the encrypted image. The projected methodology is able to implement real reversibility, that is, information extraction and image recovery area unit free of any error. This methodology embedds larger payloads for constant image quality than the antecedently used techniques, like for PSNR= 40db.
IRJET- Encryption and Authentication of Image by using Data HidingIRJET Journal
The document discusses a proposed system for encrypting and authenticating images using data hiding. At the sender side, a palette image is encrypted using an encryption key. Data is then hidden in the encrypted image using a data hiding key. At the receiver side, the image is decrypted and the original image can be reconstructed after extracting the hidden data. The system aims to securely transfer images from sender to receiver while protecting the owner's privacy and allowing accurate recovery of the original image content. Several existing reversible data hiding and encrypted image techniques are reviewed and the proposed system is described as providing satisfactory data hiding capacity while maintaining high image quality after decryption.
A Review on Reversible Data Hiding Scheme by Image Contrast EnhancementIJERA Editor
In present world demand of high quality images, security of the information on internet is one of the most important issues of research. Data hiding is a method of hiding a useful information by embedding it on another image (cover image) to provide security and only the authorize person is able to extract the original information from the embedding data. This paper is a review which describes several different algorithms for Reversible Data Hiding (RDH). Previous literature has shown that histogram modification, histogram equalization (HE) and interpolation are the most common methods for data hiding. To improve security these methods are used in encrypted images. This paper is a comprehensive study of all the major reversible data hiding approaches implemented as found in the literature.
A secure image steganography based on JND model IJECEIAES
This document presents a new secure image steganography method based on the Just Noticeable Difference (JND) model. The method uses a modified matrix encoding technique to embed secret bits into cover images. It selects pixels for embedding by calculating the JND value and average gradient of each pixel, and choosing the pixel associated with the smallest estimated embedding distortion. Experimental results on a database of 10,000 images show that stego images produced with the proposed approach achieve higher perceptual quality and security than previous approaches.
IRJET - Symmetric Image Registration based on Intensity and Spatial Informati...IRJET Journal
This document presents a proposed system for symmetric image registration based on intensity and spatial information using a technique called the Coloured Simple Algebraic Algorithm (CSAA). The system first preprocesses color images, extracts features, then classifies images as symmetric or asymmetric using a neural network. It is shown to provide accurate and robust registration of medical and biomedical images. The system is implemented and evaluated on sample images, demonstrating it can successfully identify symmetric versus asymmetric images. The proposed approach aims to improve on existing techniques for intensity-based image registration tasks.
Security and imperceptibility improving of image steganography using pixel al...IJECEIAES
Information security is one of the main aspects of processes and methodologies in the technical age of information and communication. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography and cryptography. An effective digital image-steganographic method based on odd/even pixel allocation and random function to increase the security and imperceptibility has been improved. This lately developed outline has been verified for increasing the security and imperceptibility to determine the existent problems. Huffman coding has been used to modify secret data prior embedding stage; this modified equivalent secret data that prevent the secret data from attackers to increase the secret data capacities. The main objective of our scheme is to boost the peak-signal-to-noise-ratio (PSNR) of the stego cover and stop against any attack. The size of the secret data also increases. The results confirm good PSNR values in addition of these findings confirmed the proposed method eligibility.
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.
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.
Reversible Encrypytion and Information ConcealmentIJERA Editor
Recently, a lot of attention is paid to reversible data hiding (RDH) in encrypted pictures, since it maintains the wonderful property that the initial image cover will be losslessly recovered when embedded data is extracted, whereas protects the image content that is need to be kept confidential. Other techniques used antecedently are to embed data by reversibly vacating area from the pictures, that area unit been encryted, may cause some errors on information extraction or image restoration. In this paper, we propose a unique methodology by reserving room before secret writing (i.e reserving room before encryption) with a conventional RDH algorithmic rule, and thus it becomes straightforward for hider to reversibly embed data in the encrypted image. The projected methodology is able to implement real reversibility, that is, information extraction and image recovery area unit free of any error. This methodology embedds larger payloads for constant image quality than the antecedently used techniques, like for PSNR= 40db.
IRJET- Encryption and Authentication of Image by using Data HidingIRJET Journal
The document discusses a proposed system for encrypting and authenticating images using data hiding. At the sender side, a palette image is encrypted using an encryption key. Data is then hidden in the encrypted image using a data hiding key. At the receiver side, the image is decrypted and the original image can be reconstructed after extracting the hidden data. The system aims to securely transfer images from sender to receiver while protecting the owner's privacy and allowing accurate recovery of the original image content. Several existing reversible data hiding and encrypted image techniques are reviewed and the proposed system is described as providing satisfactory data hiding capacity while maintaining high image quality after decryption.
A Review on Reversible Data Hiding Scheme by Image Contrast EnhancementIJERA Editor
In present world demand of high quality images, security of the information on internet is one of the most important issues of research. Data hiding is a method of hiding a useful information by embedding it on another image (cover image) to provide security and only the authorize person is able to extract the original information from the embedding data. This paper is a review which describes several different algorithms for Reversible Data Hiding (RDH). Previous literature has shown that histogram modification, histogram equalization (HE) and interpolation are the most common methods for data hiding. To improve security these methods are used in encrypted images. This paper is a comprehensive study of all the major reversible data hiding approaches implemented as found in the literature.
A secure image steganography based on JND model IJECEIAES
This document presents a new secure image steganography method based on the Just Noticeable Difference (JND) model. The method uses a modified matrix encoding technique to embed secret bits into cover images. It selects pixels for embedding by calculating the JND value and average gradient of each pixel, and choosing the pixel associated with the smallest estimated embedding distortion. Experimental results on a database of 10,000 images show that stego images produced with the proposed approach achieve higher perceptual quality and security than previous approaches.
IRJET - Symmetric Image Registration based on Intensity and Spatial Informati...IRJET Journal
This document presents a proposed system for symmetric image registration based on intensity and spatial information using a technique called the Coloured Simple Algebraic Algorithm (CSAA). The system first preprocesses color images, extracts features, then classifies images as symmetric or asymmetric using a neural network. It is shown to provide accurate and robust registration of medical and biomedical images. The system is implemented and evaluated on sample images, demonstrating it can successfully identify symmetric versus asymmetric images. The proposed approach aims to improve on existing techniques for intensity-based image registration tasks.
Reversible Image Data Hiding with Contrast EnhancementIRJET Journal
This document proposes a reversible image data hiding technique with contrast enhancement. It aims to embed data into a cover image in a reversible manner while also enhancing the contrast of the cover image. The technique first calculates prediction errors of pixel values in the cover image. It then generates a histogram of the prediction errors and selects carriers for data embedding from peaks in the histogram. Binary secret data is embedded into the carriers by dynamically shifting the prediction error histogram. This allows data to be embedded while increasing cover image quality compared to other reversible data hiding methods. The original cover image can be recovered by extracting the embedded data and reversing the histogram shifts. The technique is meant to achieve a higher peak signal-to-noise ratio than the original cover image after data
This document summarizes different pixel value differencing (PVD) techniques for image steganography. It discusses the original PVD method proposed in 2003 and several improvements and variations that have been proposed since, including modified PVD, adaptive PVD, modulus PVD, multi-directional PVD, multi-pixel differencing PVD using 3, 4, 5, 9 pixels, and techniques that combine PVD with LSB substitution or embedding in frequency domains. The document concludes that better techniques are needed for color images and edge-based images.
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.
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.
An image steganography using improved hyper-chaotic Henon map and fractal Tro...IJECEIAES
Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.
A new image steganography algorithm basedIJNSA Journal
In recent years, the rapid growth of information technology and digital communication has become very
important to secure information transmission between the sender and receiver. Therefore, steganography
introduces strongly to hide information and to communicate a secret data in an appropriate multimedia
carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has
been proposed to hide a large amount of secret data presented by secret color image. This algorithm is
based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the
DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach
has been applied before embedding process. The number of bit to be replaced at each byte is non uniform,
it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that
the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity
reached to four bits per byte.
Development and Comparison of Image Fusion Techniques for CT&MRI ImagesIJERA Editor
Image processing techniques primarily focus upon enhancing the quality of an image or a set ofimages to derive
the maximum information from them. Image Fusion is a technique of producing a superior quality image from a
set of available images. It is the process of combining relevant information from two or more images into a
single image wherein the resulting image will be more informative and complete than any of the input images. A
lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection,
Image processing, parallel and distributed processing, Robotics and remote sensing. This project paves way to
explain the theoretical and implementation issues of seven image fusion algorithms and the experimental results
of the same. The fusion algorithms would be assessed based on the study and development of some image
quality metrics
Two Stage Reversible Data Hiding Based On Image Interpolation and Histogram ...IJMER
In this paper a two stage reversible data hiding technique is proposed. At the first stage, an
interpolation technique is used to generate a cover image from the input image. The difference values
from input image and cover image is used as the carrier to embed data. At the second stage, a histogram
modification is applied on a difference image to embed data. The extraction process also works in two
stages. The proposed algorithm is expected to increase the embedding capacity as two techniques are
combined. The interpolation technique helps to keep the distortion low. Experimental results show that
the new method has higher embedding capacity than other existing methods.
A NEW IMAGE STEGANOGRAPHY ALGORITHM BASED ON MLSB METHOD WITH RANDOM PIXELS S...IJNSA Journal
In recent years, the rapid growth of information technology and digital communication has become very important to secure information transmission between the sender and receiver. Therefore, steganography introduces strongly to hide information and to communicate a secret data in an appropriate multimedia carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has been proposed to hide a large amount of secret data presented by secret color image. This algorithm is based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach has been applied before embedding process. The number of bit to be replaced at each byte is non uniform, it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity reached to four bits per byte.
41 9147 quantization encoding algorithm based edit tyasIAESIJEECS
In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.
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.
1. The document discusses data hiding techniques for images, specifically uniform embedding. It reviews existing methods like LSB substitution and proposes developing a new technique to select pixels for embedding, reduce embedded text size, and increase confidentiality.
2. It surveys related work on minimizing distortion in steganography, a modified matrix encoding technique for low distortion, and designing adaptive steganographic schemes.
3. The objectives are to develop a new pixel selection technique for embedding, reduce embedded text size, and increase resistance to extraction through high confidentiality. The significance is providing a solution to digital image steganography problems and focusing on choosing pixels to embed text under conditions.
Quality assessment of resultant images after processingAlexander Decker
This document discusses quality assessment of images after processing. It provides an overview of traditional perceptual image quality assessment approaches, which are based on measuring errors between distorted and reference images. These methods involve channel decomposition, error normalization based on visual sensitivity, and error pooling. The document also discusses information theoretic approaches to quality assessment, which view it as an information fidelity problem rather than just a signal fidelity problem. These approaches relate visual quality to the mutual information shared between the reference and test images. However, these methods make assumptions that are difficult to validate.
A comparative case study on the various techniques of reversible data hidingIJLT EMAS
Secured transmission of data is an important concern
now a days. A number of techniques are available for hiding and
transmitting the data through the images. It may be a simple or a
complex technique. In the receiver part if the host image is
recovered without any loss it is said to be reversible data hiding.
In this paper it is proposed to compare the reversible and lossless
data hiding techniques where both the host image and the hidden
data can be recovered without any loss. There are many
techniques that is presented as block based, pixel based and
interpolation based techniques. In the past data was embedded in
encrypted images with irreversible data hiding and now it is
reversible .The PSNR and the data hiding capacity que for these
methods are compared in this paper.
IRJET- An Improvised Multi Focus Image Fusion Algorithm through QuadtreeIRJET Journal
The document proposes a new quadtree-based algorithm for multi-focus image fusion. The algorithm divides the input images into 4 equal blocks using a quadtree structure. It then further divides each block into smaller blocks and detects the focused regions in each block using a focus measure and weighted values. The small blocks are then fused using a modified Laplacian mechanism. The fused image is evaluated using SSIM and ESSIM values, which indicate the proposed algorithm performs better fusion than previous methods.
Applying Information Security to Healthcare ServicesMarwa Hassan
The document reviews several methods for hiding electronic patient record (EPR) data within medical images. It summarizes each method's approach, key parameters like capacity and imperceptibility, strengths and limitations. Overall, it finds that methods often trade off between capacity, imperceptibility and security, with some focusing on capacity at the cost of security. Future research should optimize all three parameters to produce a secure, diagnostically reliable scheme with large enough capacity for growing EPR 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.
The document proposes a new method for enhancing security in data hiding using radiographic images. The method applies Burrows-Wheeler Transform (BWT) to distort the original data before encoding and hiding it in a cover image. BWT groups similar data patterns, distorting the original data. The decoding process decodes from the safe format and applies Inverse BWT to retrieve the original data from the stego image, realizing a two-level security scheme. Analysis shows the stego image is less deviated from the original cover image, with satisfactory quality metrics, while cryptanalysis of the hidden data is more difficult due to the original data distortion.
This document discusses image steganography using convolutional neural networks (CNNs). It begins with an abstract that introduces image steganography and its goal of concealing data within images without detection. It then provides background on traditional steganography techniques and how CNNs are now being used.
The document is a research paper that reviews various CNN-based image steganography methods. It categorizes existing approaches as traditional, CNN-based, or GAN-based. It summarizes several representative methods under each category and compares their advantages and disadvantages.
The proposed method is then described. It uses three CNNs - a preparation network to transform the secret image, a hiding network to generate a container image by combining the prepared
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
We follow "Rigorous Publication" model - means that all articles appear on IJERD after full appraisal, effectiveness, legitimacy and reliability of research content. International Journal of Engineering Research and Development publishes papers online as well as provide hard copy of Journal to authors after publication of paper. It is intended to serve as a forum for researchers, practitioners and developers to exchange ideas and results for the advancement of Engineering & Technology.
Call for Papers - 5th International Conference on Cloud, Big Data and IoT (CB...ijistjournal
5th International Conference on Cloud, Big Data and IoT (CBIoT 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and IoT.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Cloud, Big Data and IoT.
PERFORMANCE ANALYSIS OF PARALLEL IMPLEMENTATION OF ADVANCED ENCRYPTION STANDA...ijistjournal
Cryptography is the study of mathematical techniques related to aspects of information security such as confidentiality, data integrity, entity authentication, and data origin authentication. Most cryptographic algorithms function more efficiently when implemented in hardware than in software running on single processor. However, systems that use hardware implementations have significant drawbacks: they are unable to respond to flaws discovered in the implemented algorithm or to changes in standards. As an alternative, it is possible to implement cryptographic algorithms in software running on multiple processors. However, most of the cryptographic algorithms like DES (Data Encryption Standard) or 3DES have some drawbacks when implemented in software: DES is no longer secure as computers get more powerful while 3DES is relatively sluggish in software. AES (Advanced Encryption Standard), which is rapidly being adopted worldwide, provides a better combination of performance and enhanced network security than DES or 3DES by being computationally more efficient than these earlier standards. Furthermore, by supporting large key sizes of 128, 192, and 256 bits, AES offers higher security against brute-force attacks.
In this paper, AES has been implemented with single processor. Then the result has been compared with parallel implementations of AES with 2 varying different parameters such as key size, number of rounds and extended key size, and show how parallel implementation of the AES offers better performance yet flexible enough for cryptographic algorithms.
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This document proposes a reversible image data hiding technique with contrast enhancement. It aims to embed data into a cover image in a reversible manner while also enhancing the contrast of the cover image. The technique first calculates prediction errors of pixel values in the cover image. It then generates a histogram of the prediction errors and selects carriers for data embedding from peaks in the histogram. Binary secret data is embedded into the carriers by dynamically shifting the prediction error histogram. This allows data to be embedded while increasing cover image quality compared to other reversible data hiding methods. The original cover image can be recovered by extracting the embedded data and reversing the histogram shifts. The technique is meant to achieve a higher peak signal-to-noise ratio than the original cover image after data
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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.
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.
An image steganography using improved hyper-chaotic Henon map and fractal Tro...IJECEIAES
Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.
A new image steganography algorithm basedIJNSA Journal
In recent years, the rapid growth of information technology and digital communication has become very
important to secure information transmission between the sender and receiver. Therefore, steganography
introduces strongly to hide information and to communicate a secret data in an appropriate multimedia
carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has
been proposed to hide a large amount of secret data presented by secret color image. This algorithm is
based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the
DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach
has been applied before embedding process. The number of bit to be replaced at each byte is non uniform,
it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that
the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity
reached to four bits per byte.
Development and Comparison of Image Fusion Techniques for CT&MRI ImagesIJERA Editor
Image processing techniques primarily focus upon enhancing the quality of an image or a set ofimages to derive
the maximum information from them. Image Fusion is a technique of producing a superior quality image from a
set of available images. It is the process of combining relevant information from two or more images into a
single image wherein the resulting image will be more informative and complete than any of the input images. A
lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection,
Image processing, parallel and distributed processing, Robotics and remote sensing. This project paves way to
explain the theoretical and implementation issues of seven image fusion algorithms and the experimental results
of the same. The fusion algorithms would be assessed based on the study and development of some image
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Two Stage Reversible Data Hiding Based On Image Interpolation and Histogram ...IJMER
In this paper a two stage reversible data hiding technique is proposed. At the first stage, an
interpolation technique is used to generate a cover image from the input image. The difference values
from input image and cover image is used as the carrier to embed data. At the second stage, a histogram
modification is applied on a difference image to embed data. The extraction process also works in two
stages. The proposed algorithm is expected to increase the embedding capacity as two techniques are
combined. The interpolation technique helps to keep the distortion low. Experimental results show that
the new method has higher embedding capacity than other existing methods.
A NEW IMAGE STEGANOGRAPHY ALGORITHM BASED ON MLSB METHOD WITH RANDOM PIXELS S...IJNSA Journal
In recent years, the rapid growth of information technology and digital communication has become very important to secure information transmission between the sender and receiver. Therefore, steganography introduces strongly to hide information and to communicate a secret data in an appropriate multimedia carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has been proposed to hide a large amount of secret data presented by secret color image. This algorithm is based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach has been applied before embedding process. The number of bit to be replaced at each byte is non uniform, it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity reached to four bits per byte.
41 9147 quantization encoding algorithm based edit tyasIAESIJEECS
In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.
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.
1. The document discusses data hiding techniques for images, specifically uniform embedding. It reviews existing methods like LSB substitution and proposes developing a new technique to select pixels for embedding, reduce embedded text size, and increase confidentiality.
2. It surveys related work on minimizing distortion in steganography, a modified matrix encoding technique for low distortion, and designing adaptive steganographic schemes.
3. The objectives are to develop a new pixel selection technique for embedding, reduce embedded text size, and increase resistance to extraction through high confidentiality. The significance is providing a solution to digital image steganography problems and focusing on choosing pixels to embed text under conditions.
Quality assessment of resultant images after processingAlexander Decker
This document discusses quality assessment of images after processing. It provides an overview of traditional perceptual image quality assessment approaches, which are based on measuring errors between distorted and reference images. These methods involve channel decomposition, error normalization based on visual sensitivity, and error pooling. The document also discusses information theoretic approaches to quality assessment, which view it as an information fidelity problem rather than just a signal fidelity problem. These approaches relate visual quality to the mutual information shared between the reference and test images. However, these methods make assumptions that are difficult to validate.
A comparative case study on the various techniques of reversible data hidingIJLT EMAS
Secured transmission of data is an important concern
now a days. A number of techniques are available for hiding and
transmitting the data through the images. It may be a simple or a
complex technique. In the receiver part if the host image is
recovered without any loss it is said to be reversible data hiding.
In this paper it is proposed to compare the reversible and lossless
data hiding techniques where both the host image and the hidden
data can be recovered without any loss. There are many
techniques that is presented as block based, pixel based and
interpolation based techniques. In the past data was embedded in
encrypted images with irreversible data hiding and now it is
reversible .The PSNR and the data hiding capacity que for these
methods are compared in this paper.
IRJET- An Improvised Multi Focus Image Fusion Algorithm through QuadtreeIRJET Journal
The document proposes a new quadtree-based algorithm for multi-focus image fusion. The algorithm divides the input images into 4 equal blocks using a quadtree structure. It then further divides each block into smaller blocks and detects the focused regions in each block using a focus measure and weighted values. The small blocks are then fused using a modified Laplacian mechanism. The fused image is evaluated using SSIM and ESSIM values, which indicate the proposed algorithm performs better fusion than previous methods.
Applying Information Security to Healthcare ServicesMarwa Hassan
The document reviews several methods for hiding electronic patient record (EPR) data within medical images. It summarizes each method's approach, key parameters like capacity and imperceptibility, strengths and limitations. Overall, it finds that methods often trade off between capacity, imperceptibility and security, with some focusing on capacity at the cost of security. Future research should optimize all three parameters to produce a secure, diagnostically reliable scheme with large enough capacity for growing EPR 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.
The document proposes a new method for enhancing security in data hiding using radiographic images. The method applies Burrows-Wheeler Transform (BWT) to distort the original data before encoding and hiding it in a cover image. BWT groups similar data patterns, distorting the original data. The decoding process decodes from the safe format and applies Inverse BWT to retrieve the original data from the stego image, realizing a two-level security scheme. Analysis shows the stego image is less deviated from the original cover image, with satisfactory quality metrics, while cryptanalysis of the hidden data is more difficult due to the original data distortion.
This document discusses image steganography using convolutional neural networks (CNNs). It begins with an abstract that introduces image steganography and its goal of concealing data within images without detection. It then provides background on traditional steganography techniques and how CNNs are now being used.
The document is a research paper that reviews various CNN-based image steganography methods. It categorizes existing approaches as traditional, CNN-based, or GAN-based. It summarizes several representative methods under each category and compares their advantages and disadvantages.
The proposed method is then described. It uses three CNNs - a preparation network to transform the secret image, a hiding network to generate a container image by combining the prepared
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MULTIPLE CAUSAL WINDOW BASED REVERSIBLE DATA EMBEDDING
1. International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.3, May 2012
DOI : 10.5121/ijist.2012.2303 41
MULTIPLE CAUSAL WINDOW BASED REVERSIBLE
DATA EMBEDDING
Mr.R.Vinod1
, Mr.S.Pradeep2
, Mr.V.Vignesh3
, Prof.P.Tamije Selvy4
,
Ms.P.Anantha Prabha5
1,2,3
UG Student, 4
Assistant Professor(SG), 5
Assistant Professor
1,2,3,4,5
Department of Computer Science and Engineering, Sri Krishna College of
Technology, Coimbatore, India
1
vinod.200691@gmail.com,2
pradeepsens@yahoo.com,3
vignesh.060691gmail.com,
4
tamijeselvy@gmail.com,5
ap.prabha@gmail.com
ABSTRACT
Reversible data embedding is a technique that embeds data into an image in a reversible manner. An
important aspect of reversible data embedding is to find embedding area in the image and to embed the
data into it. In the conventional reversible techniques, the visual quality is not taken into account which
resulted in a poor quality of the embedded images. Hence the histogram modification based reversible data
hiding technique using multiple causal windows is proposed which predicts the embedding level with the
help of the pixel value, edge value, Just Noticeable Difference(JND) value. Using this data embedding level
the data is embedded into the pixels. The pixel level adjustment considering the Human Visual System
(HVS) characteristics is also made to reduce the distortion caused by data embedding. This significantly
improves the data embedding capacity along with greater visual quality. The proposed method includes
three phases: (i).Construction of casual window and calculation of edge and JND values in which the
casual window determines the pixel values, the edge and the JND values are calculated (ii).Data
embedding which is the process of embedding the data into the original image (iii). Data extractor and
image recovery where the original image is recovered and the embedded bits are obtained. The
experimental results and performance comparison with other reversible data hiding algorithms are
presented to demonstrate the validity of the proposed algorithm. The experimental results show that the
Performance of the proposed system on an average shows an accuracy of 95%.
Keywords
Data hiding, human visual system, just noticeable difference, lossless watermarking.
1. INTRODUCTION
Data embedding is a method of embedding confidential information into a set of host data, such
as a photograph, television signal, or identification card. Data embedding has direct applications
in data mining, data indexing and searching, information retrieval, and multimedia data
processing. Data embedding on image can be implemented as a steganography technique.
STEGANOGRAPHY is the art and science of writing hidden messages on any cover media in
such a way that no one, apart from the sender and intended recipient, suspects the existence of the
message. Reversible data embedding, which is often referred to as lossless or invertible data
embedding, is a technique that embeds data into an image in a reversible manner. Extensive
2. International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.3, May 2012
42
efforts have been devoted to increase the embedding capacity without degrading the visual quality
of the embedded image.
A key of reversible data embedding is to find an embedding area in an image with the help of
redundancy in the image content. The conventional reversible techniques [1] uses lossless data
compression to find an extra area that can contain to-be-embedded data. In order to expand the
extra space, the recent algorithms reduce the redundancy by performing pixel value prediction [1]
and/or utilizing image histogram [8]. The state-of-the-art techniques [3], [8] exhibit high
embedding capacity without severely degrading the visual quality of the embedded result.
However the visual quality is not taken into account in the conventional methods the quality of
the resultant embedded image is not satisfactory. From all data embedding techniques one
common drawback is the fact that the original image is distorted by some small amount of noise
due to data embedding itself.
The proposed system uses image as the input and a data embedding level is adaptively adjusted
for each pixel with a consideration of the human visual system (HVS) characteristics. The
conventional histogram modification methods embed a message bit into the histogram of pixel
values or the histogram of the pixel differences. A multiple causal window is constructed to
determine the pixel value of the given image and the edge value is determined. After the edge
detection is performed, the JND value is calculated. Data embedding is the process of embedding
the confidential information into the original image. The data can be embedded only in non-edge
pixel positions hence the embedding level for the edge and non-edge pixel positions are obtained.
After estimating the edge, JND value and the embedding level the message bit b is embedded for
each pixel. At the data extractor the same values are obtained and the image is recovered.
2. RELATED WORK
C.-C. Chang, Lin, Chen Y.H [1], proposed, a reversible data-embedding scheme to embed secret
information in original images. Here, an embedded pixel value is generalised according to a
predetermined threshold and the difference between the predicted pixel value and its original
pixel value. The results show that the proposed scheme can provide great payload capacity while
preserving the quality of the stego-image D.-M. Thodi, Rodrigues J.J [2], proposed, a histogram
shifting technique as an alternative to embedding the location map. This technique improves the
distortion performance at low embedding capacities and mitigates the capacity control problem.
Also a reversible data-embedding technique called prediction-error expansion is proposed here.
This new technique better exploits the correlation inherent in the neighbourhood of a pixel than
the difference-expansion scheme. Prediction-error expansion and histogram shifting combine to
form an effective method for data embedding. The results show that prediction-error expansion
doubles the maximum embedding capacity when compared to difference expansion. There is also
a significant improvement in the quality of the watermarked image, especially at moderate
embedding capacities Y. Hu, H.-K. Lee and J.Li [3], focus on improving the overflow location
map. A new embedding scheme that helps us construct an efficient payload-dependent overflow
location map is designed here. Such an overflow location map has good compressibility. The
accurate capacity control capability also reduces unnecessary alteration to the image. Thus
improves the embedding capacity with good image quality.
W. Lin, L. Dong, and P. Xue [4], proposed a method to discriminate pixel differences according to
their impact toward perceived visual quality. Local contrast changes which are noticeable are
formulated since contrast is the basic sensory feature in the human visual system (HVS)
perception. Thus the fundamental improvement in sharpened image edges is achieved by taking
the visual signal with blurring and luminance fluctuations into account.
3. International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.3, May 2012
43
S. Weng, Y.Zhao, J.-S. Pan, and Zhicheng Ni [9], proposed, reversible data hiding scheme which
uses zero or minimum points of the histogram of the image and slightly modifies the pixel values
to embed data into the image without considering the embedding level resulting with
comparatively low distortion of the original image.
3. PROPOSED METHODOLOGY
The proposed scheme is based on finding embedding area in the image and to embed the data into
it. This embedding area in the image is determined with the help of embedding level of each
pixel. Multi causal windows are used here to determine the embedding level. The perceptual
characteristic of Human Visual System is considered to improve quality degradation caused by
data embedding. The flow diagram of the proposed system is as follows.
INPUT ORIGINAL IMAGE
Input string as
binary bit
Output bits
OUTPUT ORIGINAL IMAGE
Figure 1. Overall process of Reversible Data Embedding
3.1 Construction of Multi Causal Window:
Causal window is a part of the original image with specified window size. This causal window is
used to determine the pixel values, the edge values, and the JND (Just Noticeable Difference)
values. Using these values the embedding level of each pixel is determined and the data is being
embedded into the original image. Similarly multiple causal windows are identified in the original
image and the embedding level calculation is made so that more data embedding is possible.
Causal window to predict pixel values
Determine Edge and JND values
Determine the embedding level
Embed the bit ‘b’
Bit extraction
Image recovery
EMBEDDED IMAGE
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Algorithm:
Step-1: Calculating Pixel value:
Let (i, j) denote the pixel coordinate of the original image. Let the original image be ‘x’
and the embedded image be ‘y’. The pixel value for each causal window is predicted.
x1(i,j) = 1/ N(Ωi,j) ∑ x(m,n) -(1)
(m,n)Ωi,j
where Ωi,j denotes a single causal window surrounding x(i, j)
N(Ωi,j) denotes the cardinality of the set Ωi,j.
The size of the each causal window is specified as S. The average of the pixel values is used as
the predicted value.
Step-2: Calculating Pixel Difference:
The pixel difference is calculated between the original pixel values and the predicted values as
follows,
d(i,j)=| x(i,j)- x1 (i,j) | -(2)
where d(i,j) denotes the pixel difference value for each pixel. This pixel
difference value is used in data embedding process.
Step-3: Calculating Edge Value:
The edge is simply estimated for each pixel as follows:
If var(Ωi,j) > Te , E(i,j)= 1 -(3)
If var(Ωi,j) <= Te , E(i,j)= 0 -(4)
where E(i,j) determines whether the pixel is edge or not.
var(Ωi,j) denotes the variance of pixel values in Ωi,j.
Te represents the edge threshold value.
Step-4: Calculating JND Value:
The just noticeable difference value is estimated after the edge detection as follows:
jnd(i,j) = Tl(i,j) + µ(Ta(i,j)/ Tl(i,j)) -(5)
where µ=0.5, Tl and Ta are the two thresholds representing the luminance adaptation and the
activity masking of the Human Visual System characteristics respectively.
Tl is calculated by measuring the background luminance with the help of average value of local
neighbourhood of a single causal window.
A piecewise linear approximation is used with three parameters, a, b, c .
If edge (E(i,j) = 1), a=8, b=18, c=22 -(6)
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If non-edge (E(i,j) = 0), a=10, b=20, c=24 -(7)
i.e., if the background luminance is <=75 , Tl is calculated as
Tl = b-(((b-a)/75)*background luminance)
And if the background luminance is >=125 , Tl is calculated as
Tl = (((c-a)/130)*background luminance) + ((225/130)*a)-((125/130)*c)
Ta is calculated as the maximum pixel difference value in the local neighbourhood of a single
causal window.
Step-5: Finding Embedding Level:
After calculating the edge and JND value, the embedding level of the image is determined. The
data embedding is performed by using the difference value and finding the extra space in the
image where the data can be embedded. If the embedded value exceeds a pixel value bound (0 to
255 in 8 bit images), overflow and underflow problem occurs. To solve this problem, the original
image histogram is shrunk from both sides by 2L
, where L is the embedding level. The overhead
information describing the pre-processing is losslessly compressed and embedded together with
pure payload data to realize reversible data embedding. Compared to Tai et al.’s method of
adopting the fixed embedding level, the embedding level here is adaptively adjusted for each
pixel according to the local image characteristics. The embedding level Ki,j is calculated as
follows:
If non-edge (E(i,j)= 0),
Ki,j =arg k max 2k
, where 2k
< jnd(i,j), k ≤ L -(8)
That is the maximum possible embedding level is chosen such that the pixel value change should
be lower than the JND value because the distortion above the JND in the smooth region is
perceptually disturbing.
If edge (E(i,j)= 1),
Ki,j =arg k min 2k
, where 2k
> jnd(i,j), k ≤ L -(9)
That is the minimum possible embedding level above the JND is used to embed a sufficient
amount of data because it is difficult to find the extra space in the image using the embedding
level lower than the JND value since the difference values d(i,j) in the edge region are high. The
increase of the JND value in the edge region does not reduce the visual quality. Sometimes an
intentional increase of the JND in the edge region is employed for enhancement of the image.
3.2 Data Embedding:
After the estimation of the embedding level, the data in the form of binary bits are embedded into
each pixel. The input data is converted into ASCII values and then converted into its respective
binary values. Thus the input data bit ‘b’ is embedded into pixels if d(i,j) < 2Ki,j
as follows:
If x(i,j) ≥ x1 (i,j) ,
y(i,j)= x(i,j) + d(i,j) + b -(10)
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If x(i,j) < x1 (i,j) ,
y(i,j)= x(i,j) - d(i,j) – b -(11)
If d(i,j) ≥ 2Ki,j
, the bit ‘b’ is not embedded into pixels instead pixel level adjustment is performed
as follows:
If x(i,j) ≥ x1 (i,j) ,
y(i,j)= x(i,j) + 2Ki,j
-(12)
If x(i,j) < x1 (i,j) ,
y(i,j)= x(i,j) - 2Ki,j
-(13)
This process continues until all the bits are embedded. The overflow and underflow problem is
eliminated by shifting the pixel values by 2L
at the pre-processing stage. The same is performed
for multiple causal windows to embed more number of bits. The upper and left image boundaries
are not modified to satisfy the reversibility. Finally the embedded image ‘y’ is obtained.
3.3 Data Extraction and Image Recovery:
3.3.1 Data Extraction:
Data extraction is the process of extracting the embedded data from the embedded image and to
obtain the original image. Knowing the embedding level L of the image the data is extracted from
the embedded image. Since the causal windows are used to determine edge, JND, and embedding
level, the same values can be obtained at the extractor side.
If | y(i,j)- x1 (i,j) | < 2Ki,j+1
, the data bit is extracted as follows:
If | y(i,j)- x1 (i,j) | is even, then b=0 -(14)
If | y(i,j)- x1 (i,j) | is odd, then b=1 -(15)
3.3.2 Pixel Recovery:
The original pixel values are recovered by the pixel recovery process as follows:
When | y(i,j)- x1 (i,j) | < 2Ki,j+1
, the pixel values are recovered by,
If y(i,j) < x1 (i,j) ,
x(i,j) = y(i,j) + ((ceil(y(i,j) – x1(i,j))/2 -(16)
If y(i,j) ≥ x1 (i,j) ,
x(i,j) = y(i,j) - ((ceil(y(i,j) – x1(i,j))/2 -(17)
When | y(i,j)- x1 (i,j) | ≥ 2Ki,j+1
, the pixel values are recovered by,
If y(i,j) < x1 (i,j) ,
x(i,j) = y(i,j) + 2Ki,j
-(18)
If y(i,j) > x1 (i,j) ,
x(i,j) = y(i,j) - 2Ki,j
-(19)
Thus the original pixel values including the overhead information bits at preprocessing are
extracted and finally the original image is recovered completely.
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4. EXPERIMENTAL RESULTS
The performance of the proposed system is evaluated using MATLAB. The input image is given
and the causal window is identified in the input image. The pixel values of the causal window are
determined and the average value of the pixel values is calculated.
Figure 4.1.Input image showing single causal window
The difference values for each pixel are calculated and thereby edge value is calculated using the
edge threshold Te (a threshold value which lies between 0 and 1).
Figure 4.2.Edge Value
The Just Noticeable Difference value is calculated using two thresholds representing the
luminance adaptation and the activity masking i.e., Tl and Ta (from Step-4) of the Human Visual
System characteristics respectively.
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Figure 4.3.Just Noticeable Difference Value
Using the edge and JND value, the embedding level Ki,j (from (8) and (9)) of the image is
determined.
Figure 4.4.Embedding Level Value
The input string which is to be embedded is converted into its respective bits.
Figure 4.5.Input String to Binary Conversion
Finally the data is being embedded into the pixel values (from (10) and (11)) with respect to the
embedding level and pixel level adjustment is also performed (from (12) and (13)).
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Figure 4.6.Data Embedding
Image obtained after embedding the data is displayed here.
Figure 4.7.Embedded Image
Extraction of the original data that is being embedded (from (14) and (15)).
Figure 4.8.Bit extraction
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Extracted bit is converted into its respective string.
Figure 4.9.Extracted Bit to String Conversion
The original pixel values are recovered (from (16), (17), (18) and (19)).
Figure 4.10.Original Pixel Recovery
The recovered original image.
Figure 4.11.Recovered Image
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Table 1. PERFORMANCE ANALYSIS BASED ON PSNR (Peak Signal to Noise Ratio), SSIM
(Structural Similarity), EMBEDDING CAPACITY
Figure 4.14 Performance of the system based on PSNR, SSIM, EMBEDDING CAPACITY.
The above graph shows the performance of the single and multi causal window based on the
parameters like PSNR (in dB), SSIM (in %), EMBEDDING CAPACITY (in bit per pixel).
5. CONCLUSION
Thus an improved histogram modification based reversible data hiding technique is proposed
here. Unlike the conventional reversible techniques, the HVS characteristics are considered to
avoid the distortion caused by data embedding process. The edge and JND values are estimated
by using the causal windows, and thus no additional overhead is required. By using the estimated
values, the embedding level is adaptively adjusted for each pixel. This increases the data
embedding capacity and visual quality. The experimental results show that the proposed system
produces an accuracy of about 95%. Future work includes the application of the technique to
reversible video data embedding, the video related HVS characteristics such as motion blur and
motion sharpening can be additionally considered to produce perceptually pleasant video
sequences.
SYSTEM
PSNR
(in dB)
SSIM
(in %)
EMBEDDING CAPACITY
(in bit per pixel)
SINGLE CAUSAL WINDOW 30.06 0.861 0.89
MULTI CAUSAL WINDOW 29.98 0.921 0.96
0.8
0.85
0.9
0.95
SSIM
SINGLE
CAUSAL
WINDOW
MULTIPLE
CAUSAL
WINDOW
29.9
30
30.1
PSNR
SINGLE
CAUSAL
WINDOW
MULTIPLE
CAUSAL
WINDOW
0.8
0.9
1
EMBEDDING CAPACITY
SINGLE
CAUSAL
WINDOW
MULTIPLE
CAUSAL
WINDOW
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Authors
Mr.R.Vinod is currently pursuing Bachelor of Engineering in Computer Science and
Engineering under Anna University, Coimbatore, India. His areas of interest are
Image Processing and Multimedia.
Mr.S.Pradeep is currently pursuing Bachelor of Engineering in Computer Science and
Engineering under Anna University, Coimbatore,India. His areas of interest are Image
Processing and Multimedia.
Mr.V.Vignesh is currently pursuing Bachelor of Engineering in Computer Science and
Engineering under Anna University, Coimbatore, India. His areas of interest are
Image Processing and Multimedia.
Prof. P.Tamije Selvy received B.Tech (CSE), M.Tech (CSE) in 1996 and 1998
respectively from Pondicherry University. Since 1999, she has been working as
faculty in reputed Engineering Colleges. At present, she is working as Assistant
Professor (SG) in the department of Computer Science & Engineering, Sri Krishna
College of Technology, Coimbatore. She is currently pursuing Ph.D under Anna
University, Chennai. Her Research interests include Image Processing, Data Mining,
Pattern Recognition and Artificial Intelligence.
Ms.P.Anantha Prabha obtained B.E in Electronics and Communication Engineering
and Master of Engineering in Computer Science and Engineering from V.L.B.
Janakiammal College of Engineering and Technology, Coimbatore, India in 2001 and
2008 respectively. She has been working in various Engineering Colleges for 9 years.
She is currently working as an Assistant Professor in Sri Krishna College of
Technology, Coimbatore. Her areas of interest are Clouding Computing, Mobile
Computing and Image Processing.