This document summarizes research on using the Pixel Value Differencing (PVD) steganography algorithm to hide text messages in color medical images for telemedicine applications. The PVD algorithm works by comparing the pixel values of neighboring pixels and inserting message bits based on the difference range, allowing more bits to be hidden in high contrast areas. The study tested hiding 10KB, 20KB, and 30KB texts in high and low object density medical images. For high density images, the PVD algorithm maintained a PSNR above 57.98dB for 10KB text with an MSE of 0.05 or lower. For telemedicine, PVD steganography can securely transmit confidential medical texts within color images while maintaining good image quality
Embedding Patient Information In Medical Images Using LBP and LTPcsijjournal
In this paper a new efficient interleaving methodology in which patient textual information is embedded in medical images considering Magnetic Resonance Image, Ultrasonic image and CT images of that patient body. The main disadvantages in traditional techniques to Embed patient information in medical images is inability to withstand attacks and Bit Error Rate (BER) when maximum number of characters to be embedded are absent in proposed algorithm. The New robust Embedding Techniques, Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) are used to embed the patient information in medical images. LBP Technique is mainly used to provide the security for the patient information. Along with the security high volume of patient information can be embedded inside the medical image using LTP technique. Statistical parameters such as Normalized Root Mean Square Error, PSNR (Peak Signal to Noise Ratio) are used to measure the reliability of the present technique. Experimental results strongly indicate that the present technique with zero BER (Bit Error Rate), higher PSNR (Peak Signal to Noise Ratio), and high volume of data embedding capacity achieved using LTP as compared with the other data embedding techniques. This technique is found to be robust and watermark information is recoverable without distortion.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
Framework for reversible data hiding using cost-effective encoding system for...IJECEIAES
Importance’s of reversible data hiding practices are always higher in contrast to any conventional data hiding schemes owing to its capability to generate distortion free cover media. Review of existing approaches on reversible data hiding approaches shows variable scheme mainly focusing on the embedding mechanism; however, such schemes could be furthermore improved using encoding scheme for optimal embedding performance. Therefore, the proposed manuscript discusses about a cost-effective scheme where a novel encoding scheme has been used with larger block sizes which reduces the dependencies over larger number of blocks. Further a gradient- based image registration technique is applied to ensure higher quality of the reconstructed signal over the decoding end. The study outcome shows that proposed data hiding technique is proven better than existing data hiding scheme with good balance between security and restored signal quality upon extraction of data.
Embedding Patient Information In Medical Images Using LBP and LTPcsijjournal
In this paper a new efficient interleaving methodology in which patient textual information is embedded in medical images considering Magnetic Resonance Image, Ultrasonic image and CT images of that patient body. The main disadvantages in traditional techniques to Embed patient information in medical images is inability to withstand attacks and Bit Error Rate (BER) when maximum number of characters to be embedded are absent in proposed algorithm. The New robust Embedding Techniques, Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) are used to embed the patient information in medical images. LBP Technique is mainly used to provide the security for the patient information. Along with the security high volume of patient information can be embedded inside the medical image using LTP technique. Statistical parameters such as Normalized Root Mean Square Error, PSNR (Peak Signal to Noise Ratio) are used to measure the reliability of the present technique. Experimental results strongly indicate that the present technique with zero BER (Bit Error Rate), higher PSNR (Peak Signal to Noise Ratio), and high volume of data embedding capacity achieved using LTP as compared with the other data embedding techniques. This technique is found to be robust and watermark information is recoverable without distortion.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
Framework for reversible data hiding using cost-effective encoding system for...IJECEIAES
Importance’s of reversible data hiding practices are always higher in contrast to any conventional data hiding schemes owing to its capability to generate distortion free cover media. Review of existing approaches on reversible data hiding approaches shows variable scheme mainly focusing on the embedding mechanism; however, such schemes could be furthermore improved using encoding scheme for optimal embedding performance. Therefore, the proposed manuscript discusses about a cost-effective scheme where a novel encoding scheme has been used with larger block sizes which reduces the dependencies over larger number of blocks. Further a gradient- based image registration technique is applied to ensure higher quality of the reconstructed signal over the decoding end. The study outcome shows that proposed data hiding technique is proven better than existing data hiding scheme with good balance between security and restored signal quality upon extraction of data.
Comparative study between metaheuristic algorithms for internet of things wir...IJECEIAES
Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Intelligent Handwritten Digit Recognition using Artificial Neural NetworkIJERA Editor
The aim of this paper is to implement a Multilayer Perceptron (MLP) Neural Network to recognize and predict handwritten digits from 0 to 9. A dataset of 5000 samples were obtained from MNIST. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden units and the number of iterations. The performance was thereafter compared to obtain the network with the optimal parameters. The proposed system predicts the handwritten digits with an overall accuracy of 99.32%.
A secure image steganography based on JND model IJECEIAES
Minimizing distortion produced by embedding process is very important to improve the security of hidden message and maintain the high visual quality of stego images. To achieve these objectives, an effective strategy is to perform pixel selection which is well-known as a channel selection rule. In this approach, a pixel associated with the smallest image degradation is chosen to carry secret bits. From these facts, in this paper, a new secure channel selection rule for digital images in spatial domain is designed and proposed. In this new approach, the modified matrix embedding method is utilized as data hiding method because it introduces more than one embedding change to be performed. This enables us to select a suitable pixel to embed message bits with less degradation yielded in a stego-image. In pixel selection of the proposed method, a just noticeable difference value and gradient value of a considering pixel are employed together. The experimental results (which were conducted on 10,000 uncompressed images) indicate that stego images of the proposed approach achieve a higher perceptual quality and security than those of the stego-images created by the previous approaches.
MULTIPLE CAUSAL WINDOW BASED REVERSIBLE DATA EMBEDDINGijistjournal
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%.
Comparative Performance of Image Scrambling in Transform Domain using Sinusoi...CSCJournals
With the rapid development of technology, and the popularization of internet, communication is been greatly promoted. The communication is not limited only to information but also includes multimedia information like digital Images. Therefore, the security of digital images has become a very important and practical issue, and appropriate security technology is used for those digital images containing confidential or private information especially. In this paper a novel approach of Image scrambling has been proposed which includes both spatial as well as Transform domain. Experimental results prove that correlation obtained in scrambled images is much lesser then the one obtained in transformed 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.
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.
AN EFFECTIVE SEMANTIC ENCRYPTED RELATIONAL DATA USING K-NN MODELijsptm
Data exchange and data publishing are becoming an important part of business and academic practices.
Data owners need to maintain the rights over the datasets they share. A right-protection mechanism can be
provided for the ownership of shared data, without revealing its usage under a wide range of machine
learning and mining. In the approach provide two algorithms: the Nearest-Neighbors (NN) and determiner
preserves the Minimum Spanning Tree (MST). The K-NN protocol guarantees that relations between object
remain unaltered. The algorithms preserve the both right protection and utility preservation. The rightprotection
scheme is based on watermarking. Watermarking methodology preserves the distance
relationships.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
Steganography is going to gain its importance due to the exponential growth and secret communication of potential computer users over the internet [5]. It can also be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. Generally data embedding is achieved in communication, image, text, voice or multimedia content for copyright, military communication, authentication and many other purposes [2]. In image Steganography, secret communication is achieved to embed a message into cover image (used as the carrier to embed message into) and generate a stego- image (generated image which is carrying a hidden message)[1]. In this paper we have critically analyzed various steganographic techniques and also have covered steganography overview its major types, classification, applications [3]. KEYWORDS: STEGANOGRAPHY, STEGO IMAGE, COVER IMAGE, LSB
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.
Protecting Data by Improving Quality of Stego Image based on Enhanced Reduced...IJECEIAES
In this era of internet development, security of information sharing is the main problem faced by human being. Data hiding technique is one of the solutions. However, hiding credential information within a multimedia file such as image reduces its visual quality. Therefore, unauthorized users may suspect the existance of secret data within that image. In the past years, various data hiding algorithms have been developed by researchers to overcome the problem of high distortion of image after data embedding process. Achieving a high quality stego image, however, is still a challenging problem. In this paper, we proposed a new data hiding algorithm based on different expansion. It aims to enhance the quality of stego image for a given payload size. The new algorithm is evaluated on various medical images. Thereafter, the experimental results show that the visual quality is improved; and increasing the embedding capacity leads to more noises. Therefore, a better choice of base point and a reduced difference expansion affect the quality of stego image.
Towards A More Secure Web Based Tele Radiology System: A Steganographic ApproachCSCJournals
While it is possible to make a patient's medical images available to a practicing radiologist online e.g. through open network systems inter connectivity and email attachments, these methods don't guarantee the security, confidentiality and tamper free reliability required in a medical information system infrastructure. The possibility of securely and covertly transmitting such medical images remotely for clinical interpretation and diagnosis through a secure steganographic technique was the focus of this study.
We propose a method that uses an Enhanced Least Significant Bit (ELSB) steganographic insertion method to embed a patient's Medical Image (MI) in the spatial domain of a cover digital image and his/her health records in the frequency domain of the same cover image as a watermark to ensure tamper detection and nonrepudiation. The ELSB method uses the Marsenne Twister (MT) Pseudo Random Number Generator (PRNG) to randomly embed and conceal the patient's data in the cover image. This technique significantly increases the imperceptibility of the hidden information to steganalysis thereby enhancing the security of the embedded patient's data.
In measuring the effectiveness of the proposed method, the study adopted the Design Science Research (DSR) methodology, a paradigm for problem solving in computing and Information Systems (IS) that involves design and implementation of artefacts and methods considered novel and the analytical testing of the performance of such artefacts in pursuit of understanding and enhancing an existing method, artefact or practice.
The fidelity measures of the stego images from the proposed method were compared with those from the traditional Least Significant Bit (LSB) method in order to establish the imperceptibility of the embedded information. The results demonstrated improvements of between 1 to 2.6 decibels (dB) in the Peak Signal to Noise Ratio (PSNR), and up to 0.4 MSE ratios for the proposed method.
A new hybrid steganographic method for histogram preservation IJEEE
This paper presents a histogram preserving data embedding method for grey-scale images which is based on pixel value differencing (PVD) and least-significant-bit (LSB) substitution methods. Various PVD based steganographic methods achieve high data embedding capacity with minimum distortions in stego image at the cost of change in histogram characteristics which is can be detected by histogram based steganalysers. This persistent problem can been taken care off by proposed method of data hiding. The improved performance of the proposed method is verified through extensive simulations.
Comparative study between metaheuristic algorithms for internet of things wir...IJECEIAES
Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Intelligent Handwritten Digit Recognition using Artificial Neural NetworkIJERA Editor
The aim of this paper is to implement a Multilayer Perceptron (MLP) Neural Network to recognize and predict handwritten digits from 0 to 9. A dataset of 5000 samples were obtained from MNIST. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden units and the number of iterations. The performance was thereafter compared to obtain the network with the optimal parameters. The proposed system predicts the handwritten digits with an overall accuracy of 99.32%.
A secure image steganography based on JND model IJECEIAES
Minimizing distortion produced by embedding process is very important to improve the security of hidden message and maintain the high visual quality of stego images. To achieve these objectives, an effective strategy is to perform pixel selection which is well-known as a channel selection rule. In this approach, a pixel associated with the smallest image degradation is chosen to carry secret bits. From these facts, in this paper, a new secure channel selection rule for digital images in spatial domain is designed and proposed. In this new approach, the modified matrix embedding method is utilized as data hiding method because it introduces more than one embedding change to be performed. This enables us to select a suitable pixel to embed message bits with less degradation yielded in a stego-image. In pixel selection of the proposed method, a just noticeable difference value and gradient value of a considering pixel are employed together. The experimental results (which were conducted on 10,000 uncompressed images) indicate that stego images of the proposed approach achieve a higher perceptual quality and security than those of the stego-images created by the previous approaches.
MULTIPLE CAUSAL WINDOW BASED REVERSIBLE DATA EMBEDDINGijistjournal
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%.
Comparative Performance of Image Scrambling in Transform Domain using Sinusoi...CSCJournals
With the rapid development of technology, and the popularization of internet, communication is been greatly promoted. The communication is not limited only to information but also includes multimedia information like digital Images. Therefore, the security of digital images has become a very important and practical issue, and appropriate security technology is used for those digital images containing confidential or private information especially. In this paper a novel approach of Image scrambling has been proposed which includes both spatial as well as Transform domain. Experimental results prove that correlation obtained in scrambled images is much lesser then the one obtained in transformed 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.
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.
AN EFFECTIVE SEMANTIC ENCRYPTED RELATIONAL DATA USING K-NN MODELijsptm
Data exchange and data publishing are becoming an important part of business and academic practices.
Data owners need to maintain the rights over the datasets they share. A right-protection mechanism can be
provided for the ownership of shared data, without revealing its usage under a wide range of machine
learning and mining. In the approach provide two algorithms: the Nearest-Neighbors (NN) and determiner
preserves the Minimum Spanning Tree (MST). The K-NN protocol guarantees that relations between object
remain unaltered. The algorithms preserve the both right protection and utility preservation. The rightprotection
scheme is based on watermarking. Watermarking methodology preserves the distance
relationships.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
Steganography is going to gain its importance due to the exponential growth and secret communication of potential computer users over the internet [5]. It can also be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. Generally data embedding is achieved in communication, image, text, voice or multimedia content for copyright, military communication, authentication and many other purposes [2]. In image Steganography, secret communication is achieved to embed a message into cover image (used as the carrier to embed message into) and generate a stego- image (generated image which is carrying a hidden message)[1]. In this paper we have critically analyzed various steganographic techniques and also have covered steganography overview its major types, classification, applications [3]. KEYWORDS: STEGANOGRAPHY, STEGO IMAGE, COVER IMAGE, LSB
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.
Protecting Data by Improving Quality of Stego Image based on Enhanced Reduced...IJECEIAES
In this era of internet development, security of information sharing is the main problem faced by human being. Data hiding technique is one of the solutions. However, hiding credential information within a multimedia file such as image reduces its visual quality. Therefore, unauthorized users may suspect the existance of secret data within that image. In the past years, various data hiding algorithms have been developed by researchers to overcome the problem of high distortion of image after data embedding process. Achieving a high quality stego image, however, is still a challenging problem. In this paper, we proposed a new data hiding algorithm based on different expansion. It aims to enhance the quality of stego image for a given payload size. The new algorithm is evaluated on various medical images. Thereafter, the experimental results show that the visual quality is improved; and increasing the embedding capacity leads to more noises. Therefore, a better choice of base point and a reduced difference expansion affect the quality of stego image.
Towards A More Secure Web Based Tele Radiology System: A Steganographic ApproachCSCJournals
While it is possible to make a patient's medical images available to a practicing radiologist online e.g. through open network systems inter connectivity and email attachments, these methods don't guarantee the security, confidentiality and tamper free reliability required in a medical information system infrastructure. The possibility of securely and covertly transmitting such medical images remotely for clinical interpretation and diagnosis through a secure steganographic technique was the focus of this study.
We propose a method that uses an Enhanced Least Significant Bit (ELSB) steganographic insertion method to embed a patient's Medical Image (MI) in the spatial domain of a cover digital image and his/her health records in the frequency domain of the same cover image as a watermark to ensure tamper detection and nonrepudiation. The ELSB method uses the Marsenne Twister (MT) Pseudo Random Number Generator (PRNG) to randomly embed and conceal the patient's data in the cover image. This technique significantly increases the imperceptibility of the hidden information to steganalysis thereby enhancing the security of the embedded patient's data.
In measuring the effectiveness of the proposed method, the study adopted the Design Science Research (DSR) methodology, a paradigm for problem solving in computing and Information Systems (IS) that involves design and implementation of artefacts and methods considered novel and the analytical testing of the performance of such artefacts in pursuit of understanding and enhancing an existing method, artefact or practice.
The fidelity measures of the stego images from the proposed method were compared with those from the traditional Least Significant Bit (LSB) method in order to establish the imperceptibility of the embedded information. The results demonstrated improvements of between 1 to 2.6 decibels (dB) in the Peak Signal to Noise Ratio (PSNR), and up to 0.4 MSE ratios for the proposed method.
A new hybrid steganographic method for histogram preservation IJEEE
This paper presents a histogram preserving data embedding method for grey-scale images which is based on pixel value differencing (PVD) and least-significant-bit (LSB) substitution methods. Various PVD based steganographic methods achieve high data embedding capacity with minimum distortions in stego image at the cost of change in histogram characteristics which is can be detected by histogram based steganalysers. This persistent problem can been taken care off by proposed method of data hiding. The improved performance of the proposed method is verified through extensive simulations.
Comparative Study of Spatial Domain Image Steganography TechniquesEswar Publications
Steganography is an important area of research in information security. It is the technique of disclosing information into the cover image via. text, video, and image without causing statistically significant modification to the cover image. Secure communication of data through internet has become a main issue due to several passive and active attacks. The purpose of stegnography is to hide the existence of the message so that it becomes difficult for attacker to detect it. Different steganography techniques are implemented to hide the information effectively also researchers contributed various algorithms in each technique to improve the technique’s efficiency. In this paper we do a brief analysis of different spatial domain image stegnography techniques and their comparison. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected.
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.
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.
A Hybrid Steganography Technique Based On LSBMR And OPAPIOSRJVSP
This paper presents a steganography technique based on two existing methods of data hiding i.e. LSBMR and OPAP. The proposed method uses the non-overlapping blocks, having three consecutive pixels. The center pixel of this block embeds k- bits of secret data using OPAP and the remaining pixels of the block embed data using LSBMR. The experimental results show that the proposed method provides better embedding capacity while maintaining the good image quality. The improved performance is shown in comparison to other data hiding methods that are investigated in this study.
Implementation of LSB-Based Image Steganography Method for effectiveness of D...ijsrd.com
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Santoso 2019 j._phys.__conf._ser._1175_012057
1. Journal of Physics: Conference Series
PAPER • OPEN ACCESS
Color-based microscopic image steganography for telemedicine
applications using pixel value differencing algorithm
To cite this article: Budi Santoso 2019 J. Phys.: Conf. Ser. 1175 012057
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Published under licence by IOP Publishing Ltd
1st International Conference on Advance and Scientific Innovation (ICASI)
IOP Conf. Series: Journal of Physics: Conf. Series 1175(2019) 012057
IOP Publishing
doi:10.1088/1742-6596/1175/1/012057
1
Color-based microscopic image steganography for
telemedicine applications using pixel value differencing
algorithm
Budi Santoso
Engineering Faculty, Dr.Soetomo University, Semolowaru 84th
Surabaya, Indonesia
*budi.santoso@unitomo.ac.id
Abstract. There are some ethics in the medical world to keep patient information confidential
on every result of medical observations and analysis. Therefore, steganography technique is an
alternative to conceal sensitive text information on a medical image in the telemedicine system
process. Research on steganography using Simple LSB-Substitution and other algorithm has
been available but in this study, the implementation of steganography was designed to hide text
in color medical images before being sent through the telemedicine system. Sensitive text is
hidden in medical images using the Pixel Value Differencing (PVD) algorithm. The main
purpose of using this algorithm is to maintain the peak signal to noise ratio (PSNR) and mean
square error (MSE) of the steganography image result. As a result, the PSNR value can be
maintained at the 57,98dB for 10KB hidden text, and lower MSE at 0.05 for high-density
object microscopic image.
1. Introduction
The confidentiality of information, especially the personal data of patients and the results of medical
diagnosis, must be maintained, in accordance with the code of ethics in the medical world. Meanwhile,
the development of communication technology has made possible the process of diagnosis and remote
medical observation, where patients and medical experts are not in one place - or commonly referred
to as telemedicine. Because of the range of distance between patients and medical experts, the process
of transmitting data from diagnoses or observations through media that are already available in
communication technology is needed[1]. Media commonly used in data transmission processes are
electromagnetic waves (radio propagation) and internet networks. However, the transmission through
both media requires an additional process so the data cannot be tapped and only reaches the specified
destination.
There are many ways to secure data that can be done in the data transmission process, such as
watermarking[2][3][4], cryptography[5][6], and encryption[7][8]. But steganography is one of the
more appropriate ways to do because it can send two data formats both images and text, and also
simultaneously encoded. According to T.Morkel et.al[9], steganography is defined as the science and
art of hiding secret messages (hiding messages) so that the existence of messages is not detected by
the human senses. The media used is generally a different medium from the secret information carrier
media, this is the function of steganography as a disguise technique using different media to hide
confidential information so it is not clearly visible.
3. 1st International Conference on Advance and Scientific Innovation (ICASI)
IOP Conf. Series: Journal of Physics: Conf. Series 1175(2019) 012057
IOP Publishing
doi:10.1088/1742-6596/1175/1/012057
2
1.1. Pixel Value Differencing
Pixel Value Differencing (PVD) is one method used in steganography and operates in the spatial
domain. Early research on PVD algorithms was carried out by Wu et al., 2001, by looking for
differences in the value of the two closest pixels. The difference is used to determine the amount of
data that can be inserted based on the range of the selected table.
Based on an analysis of the human visual system which states that the human eye is not sensitive to
changes in pixels that have a high contrast but sensitive to changes in pixels that have low contrast.
Through these properties, more bits of confidential data can be inserted in pixels that have high
contrast values, and fewer bits that can be inserted in pixels with low contrast. This is the basis of the
PVD algorithm on steganography.
The PVD algorithm is a system of two-pixel closest search direction as shown in Figure 1:
Figure 1. PVD algorithm concept
The insertion process in this method is done by comparing two neighboring pixels Pi and Pi+1 using
equation (1).
𝒅𝒊 = |𝑷(𝒊) − 𝑷(𝒊+𝟏)| (1)
The results of these comparisons are used to find out how many bits can be inserted into the two
compared pixels. This method uses the Wu and Tsai schemes to determine the range of the previous
pixel comparison. The Wu and Tsai schemes used are R = {[0-7], [8-15], [16-31], [32-63], [64-127],
[128-255]}.
Table 1. A The Wu and Tsai R schemes
Range 0-7 8-15 16-31 32-63 64-127 128-255
Hiding
Capacity 3 3 4 5 6 7
This scheme is used to find out which range is the difference between the two pixels if it is known
where the range is, then we can find the lower limit (U𝒊) and upper limit (L𝒊). After knowing the lower
limit and upper limit of the range, we can calculate the width of the optimum range (Wi) using
equation (2).
𝑊𝑖 = 𝑈𝑖 − 𝐿𝑖 + 1 (2)
The next step is to find the number of message bits (t𝒊) that can be inserted through equation (2.3).
𝑡𝑖 = [log2(𝑊𝑖)] (3)
Message insertion can be done by taking as many as 𝒊 bits of the message to be inserted. After
knowing how many message bits will be inserted, change the message bits to be inserted into decimal
(). Then the new difference value is calculated for insertion into the image using equation (4).
𝑑′𝑖 = 𝑏 + 𝐿𝑖 (4)
To determine the value of new pixels that have been inserted messages, there are several rules that
must be met with equation 5:
4. 1st International Conference on Advance and Scientific Innovation (ICASI)
IOP Conf. Series: Journal of Physics: Conf. Series 1175(2019) 012057
IOP Publishing
doi:10.1088/1742-6596/1175/1/012057
3
(𝑃′
(𝑖), 𝑃′
(𝑖+1)) =
{
(𝑃(𝑖) + [
𝑚
2
], 𝑃(𝑖+1) − [
𝑚
2
]) , 𝑖𝑓 𝑃(𝑖) ≥ 𝑃(𝑖+1) 𝑎𝑛𝑑 𝑑′
𝑖 > 𝑑𝑖
(𝑃(𝑖) − [
𝑚
2
], 𝑃(𝑖+1) + [
𝑚
2
]) , 𝑖𝑓 𝑃(𝑖) < 𝑃(𝑖+1) 𝑎𝑛𝑑 𝑑′
𝑖 > 𝑑𝑖
(𝑃(𝑖) − [
𝑚
2
], 𝑃(𝑖+1) + [
𝑚
2
]) , 𝑖𝑓 𝑃(𝑖) ≥ 𝑃(𝑖+1) 𝑎𝑛𝑑 𝑑′
𝑖 ≤ 𝑑𝑖
(𝑃(𝑖) + [
𝑚
2
], 𝑃(𝑖+1) − [
𝑚
2
]) , 𝑖𝑓 𝑃(𝑖) < 𝑃(𝑖+1) 𝑎𝑛𝑑 𝑑′
𝑖 ≤ 𝑑𝑖}
Where,
𝑚 = 𝑑′𝑖 − 𝑑 (5)
These processes are carried out continuously until the message bits are all inserted into the image.
The process of extracting messages from the stego image using the PVD algorithm begins by sorting
all the pixels in the image that has been inserted into the message, according to the way the message is
retrieved. Then the difference in the value of the new difference value (𝒊) is calculated. The new
difference value is used to determine the continuous ranges (R) value that has been defined using the
Wu and Tsai schemes. Thus the message that has been inserted () is obtained. Based on this
information it can be seen how long the secret data is inserted in both pixels so the secret message that
has been inserted is retrieved.
1.2. Least Significant Bit (LSB) Steganography
Least significant bit (LSB) insertion is a simple approach to embedding text in a cover image[10]s.
The least significant bit or the 8th bit from some of the bytes inside an image is changed to a bit of the
secret message. When using a color image, a bit of each of red, green and blue color components can
be used, since they are each represented by a byte. In this study, steganography using LSB insertion is
used as a comparison of the of the PVD steganography algorithm image output. The results of the
PSNR comparison of the two methods are presented in the form of tables and graphs.
2. Result and Discussion
In this study 2 groups of test data were used from the ZN Sputum Smear Microscopy Image Database
(ZNSM-iDB). The first group contained 20 microscopic images with 1-10 bacilli (low density bacili
image) as a result of the Zielh Nielsen staining process. Whereas the second group contained 20
microscopic images with many bundled bacilli (high density bacili image) resulting from the Zielh
Nielsen staining process.
Figure 2. Low objet density
microscopic image
Figure 3. High objet density
microscopic image
In each image the process of hiding data is done with text files size of 10KB, 20KB and 30KB.
The original image is treated as a cover image at the steganography process, both for the PVD method
and the LSB method as a comparison. In each test calculation of each MSE and PSNR is carried out,
as the output image quality parameters for steganography.
5. 1st International Conference on Advance and Scientific Innovation (ICASI)
IOP Conf. Series: Journal of Physics: Conf. Series 1175(2019) 012057
IOP Publishing
doi:10.1088/1742-6596/1175/1/012057
4
2.1. Test Results of High-Density Objects (HDO)
Table 4 shows the overall results of steganography using PVD algorithm experiments for groups of
images with high object density both for MSE and PSNR measurement.
Table 2. Results of steganography using PVD for high-density object
image
PSNR (dB) MSE
text
10KB
text
20KB
text
30KB
text
10KB
text
20KB
text
30KB
01 58.0525 54.9574 53.2387 0.0527 0.1074 0.1597
02 58.3286 55.3168 53.5693 0.0491 0.0982 0.147
03 58.0522 55.077 53.3029 0.0526 0.1044 0.1572
04 58.2875 55.083 53.402 0.0496 0.1037 0.1528
05 57.7827 54.7891 53.1601 0.0553 0.1106 0.1616
06 57.9707 55.1059 53.4114 0.053 0.1029 0.1521
07 57.813 54.8026 53.0383 0.0562 0.1122 0.1685
08 58.339 55.321 53.5255 0.0492 0.0986 0.149
09 58.2085 55.2601 53.5348 0.0506 0.0994 0.148
10 58.0876 55.0981 53.4004 0.052 0.1034 0.153
11 58.2389 55.0726 53.3555 0.0503 0.1035 0.1538
12 57.8192 54.8207 53.0956 0.0561 0.1118 0.1661
13 58.041 54.9614 53.1687 0.0524 0.1064 0.1609
14 58.1478 55.0164 53.1872 0.0512 0.1049 0.1599
15 56.4596 53.5155 51.7487 0.0842 0.1648 0.2477
16 58.2181 55.1944 53.3865 0.0508 0.1022 0.1553
17 58.0486 55.1243 53.3479 0.0527 0.1037 0.1564
18 57.8385 54.8681 53.1697 0.0547 0.1084 0.1607
19 57.7224 54.7427 53.0455 0.0575 0.1141 0.1683
20 58.244 55.2506 53.4722 0.0501 0.0998 0.1502
2.2. Test Results of Low-Density Objects (LDO)
Table 3 shows the overall results of experiments for groups of images with low object density both for
MSE and PSNR measurement.
Table 3. Results of steganography using PVD for low-density object
image
PSNR (dB) MSE
text
10KB
text
20KB
text
30KB
text
10KB
text
20KB
text
30KB
01 57.3763 54.3879 52.7102 0.0628 0.125 0.1835
02 57.5083 54.5336 52.7643 0.06 0.1193 0.1793
03 57.5004 54.4378 52.6857 0.0606 0.1223 0.1831
04 57.9232 54.9458 53.1905 0.0546 0.1083 0.1622
05 58.1048 55.1562 53.3953 0.0521 0.1028 0.1542
06 57.5526 54.5602 52.8391 0.0596 0.1189 0.1768
07 57.9757 54.8825 53.0688 0.0539 0.1102 0.1677
08 57.3742 54.3887 52.6122 0.0628 0.1252 0.1884
09 57.2537 54.2082 52.4063 0.0649 0.1311 0.1987
6. 1st International Conference on Advance and Scientific Innovation (ICASI)
IOP Conf. Series: Journal of Physics: Conf. Series 1175(2019) 012057
IOP Publishing
doi:10.1088/1742-6596/1175/1/012057
5
10 57.7416 54.7735 53.0174 0.0571 0.1129 0.1691
11 57.9162 54.7335 52.9255 0.0549 0.1151 0.1747
12 57.3804 54.2713 52.4613 0.063 0.1294 0.1969
13 57.8996 54.9183 53.1964 0.0549 0.109 0.1619
14 57.764 54.8299 53.0685 0.0569 0.1117 0.1673
15 57.8527 55.0402 53.3048 0.0541 0.1059 0.1576
16 57.5291 54.4319 52.5515 0.0603 0.1238 0.192
17 58.0589 55.0337 53.2567 0.0526 0.1058 0.1596
18 57.7674 54.8931 53.1683 0.056 0.1091 0.1625
19 57.3452 54.4089 52.6515 0.0634 0.1244 0.1865
20 57.3874 54.3969 52.667 0.063 0.1252 0.186
From the test results, it was found that the average MSE and PSNR values in the PVD
steganography process are better than the results obtained from the LSB steganography method.
Whereas in testing the type of microscopic image groups, it was found that the group of images with a
low bacilli density had higher MSE values and a lower PSNR. This is advantageous because in a
microscopic image with higher bacilli density requires more observation accuracy as one of the basic
diagnoses by the medical expert.
Figure 4. Graph of PSNR PVD Steganography results on HDO
Figure 5. Graph of PSNR PVD Steganography results on LDO
48
50
52
54
56
58
60
1 3 5 7 9 11 13 15 17 19
PSNR(dB)
10kb-txt
20kb-txt
30kb-txt
49
50
51
52
53
54
55
56
57
58
59
1 3 5 7 9 11 13 15 17 19
PSNR(dB)
10kb-txt
20kb-txt
30kb-txt
7. 1st International Conference on Advance and Scientific Innovation (ICASI)
IOP Conf. Series: Journal of Physics: Conf. Series 1175(2019) 012057
IOP Publishing
doi:10.1088/1742-6596/1175/1/012057
6
Generally, -both the PVD and LSB methods- the PSNR value decreases when the size of the
message stored in the image increases as shown at Table 4.
Table 4. Average results of MSE and PSNR steganography using PVD
Average of MSE
10kb text 20kb text 30kb text
PVD Method (HDO) 0.054015 0.10802 0.16141
PVD Method (LDO) 0.058375 0.11677 0.1754
LSB Method (HDO) 0.32186 0.64036 0.956065
LSB Method (LDO) 0.32664 0.64282 0.96246
Average of PSNR (dB)
10kb text 20kb text 30kb text
PVD Method (HDO) 57.98502 54.96889 53.22805
PVD Method (LDO) 57.66059 54.66161 52.89707
LSB Method (HDO) 50.09723 47.12932 45.40128
LSB Method (LDO) 50.149575 47.19772 45.4311
3. Conclussion
Based on the results of steganography testing for microscopic color images using the PVD algorithm,
it is found that the PVD algorithm produces a stegano image with better PSNR than the comparison
method (LSB). The best average value is 57.98 dB in the 10KB text. Besides that, it is found that the
MSE measurement results on PVD algorithm steganography output with high object density images
are even lower, that is 0.05 in the 10KB text.
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