Super-resolution deep neural network (SRDNN) based multi-image steganography for highly secured lossless image transmission

Information exchange and communication through the Internet are one of the most crucial aspects of today's information technology world. The security of information transmitted online has grown to be a critical concern, particularly in the transfer of medical data. To overcome this, the data must be delivered securely without being altered or lost. This can be possibly done by combining the principles of cryptography and steganography. In the recent past, steganography is used with simpler methods like the least significant bit manipulation technique, in order to encode a lower-resolution image into a higher-resolution image. Here, we attempt to use deep neural networks to combine many two-dimensional colour images of the same resolution into a single cover image with the same resolution. In this technique, many secret images are concealed inside a single cover image using deep neural networks. The embedded cover image is then encrypted using a 3D chaotic map for diffusion and elliptic curve cryptography (ECC) for confusion to increase security.Supporting the fact that neural networks experience losses, the proposed system recovers up to 93% of the hidden image concealed in the original image. As the secret image features are identified and combined along with the cover image, the time complexity involved in the security process is minimized by 78% compared to securing the original data.

Adversarial feature hybrid framework for steganography with shifted window local loss

Image steganography is a long-standing image security problem that aims at hiding information in cover images. In recent years, the application of deep learning to steganography has the tendency to outperform traditional methods. However, the vigorous development of CNN-based steganalyzers still have a serious threat to steganography methods. To address this gap, we present an end-to-end adversarial steganography framework based on CNN and Transformer learned by shifted window local loss, called StegoFormer, which contains Encoder, Decoder, and Discriminator. Encoder is a hybrid model based on U-shaped network and Transformer block, which effectively integrates high-resolution spatial features and global self-attention features. In particular, Shuffle Linear layer is suggested, which can enhance the linear layer's competence to extract local features. Given the substantial error in the central patch of the stego image, we propose shifted window local loss learning to assist Encoder in generating accurate stego images via weighted local loss. Furthermore, Gaussian mask augmentation method is designed to augment data for Discriminator, which helps to improve the security of Encoder through adversarial training. Controlled experiments show that StegoFormer is superior to the existing advanced steganography methods in terms of anti-steganalysis ability, steganography effectiveness, and information restoration.

Double layer steganography technique using DNA sequences and images

Information security has become increasingly challenging as a result of massive advancements in information and communication technologies. Due to the necessity of exchanging private information and the open nature of the network, there is an increased risk of various types of attacks. Consequently, data security is an essential component of data communication. One of the most effective methods used to achieve secrecy is steganography. This method hides data within a cover object without raising suspicion. The level of security is improved when two steganography methods are combined. This approach is known as multilevel steganography, which hides sensitive data in two cover objects in order to provide a two-level security system. Accordingly, we developed a technique that focuses on protecting secrecy while also being robust to attacks. The new technique uses a multi-layer steganography mechanism by using DNA sequences and images as carriers for sensitive data. The technique intends to hide secret messages in the DNA using the substation algorithm, and then the fake DNA is embedded in an image utilizing the discrete cosine transform (DCT) method. Eventually, the stego image is sent to the intended recipient. Different types of images with different sizes and lengths of messages and DNA sequences were used during the experiments. The results show that the proposed mechanism is resistant to histogram and chi-square attacks. The maximum mean value observed was 0.05, which means the histograms of the original and stego images are nearly identical, and the stego image does not raise any suspicion regarding the existence of secret information. In addition, the imperceptibility ratios were good, as the highest PSNR and MSE values were 0.078 and 72.2, respectively. Finally, the PNG and BMP images show excellent results. On the other hand, the JPG images failed to meet the expected ratio of imperceptibility and security.

Enhancing Data Security of Cloud Based LMS

Around the world, the educational system is evolving. The new trend can be found in traditional classroom systems as well as digitalization systems. Cloud-based Learning Management Systems (LMS) will accelerate the educational industry forward in the next years because they can provide end-user with a versatile, convenient, secure, and cost-effective learning process. The cloud-based LMS approach is the most effective and proper learning model in the worldwide educational sector, particularly if the organization is in a state of depression owing to a global pandemic. It can be utilized over the internet with several users on the same platform. As a result, the initial requirement is important to enable to the LMS model. Despite its many advantages, LMS confronts challenges such as confidentiality, user acceptance, and traffic. In a pandemic like Covid 19, the entire planet depends on a safe LMS platform to establish student and instructor trust. Therefore, with this work, the attempt has been made to explain one LMS model that may provide its users with optimal security, a user-friendly environment, and quick access. This paper discusses the use of the cloud attack, and also cryptographic and steganographic security models and techniques to address these issues. There's also information on what kinds of security vulnerabilities or operations on cloud data are feasible, and also how to deal with them using various algorithms.

A novel steganographic technique for medical image using SVM and IWT

This study presents an efficient authentication scheme for digital image steganography on medical images benefiting from the combination of both techniques: Support Vector Machine (SVM) and Integer Wavelet Transform (IWT). We use two different strategies in this paper, where SVM is used first to separate the Region of Interest (ROI) from Non-Region of Interest (NROI) in the medical image. Then IWT is applied to embed secret information within the NROI part of the medical image (Cover Image). Moreover, we have applied a circular array and a shared secret key to enhance the robustness of the proposed scheme. The research looked into the various experimental analyses to establish the acceptability of the existing scheme. The simulation is performed to measure the imperceptibility using Peak Signal to Noise Ratio (PSNR) and to test the robustness using the Structural Similarity Index Measure (SSIM). The experimental result shows good imperceptibility with a PSNR of 64 dB and better robustness with a SSIM of 0.96 for the proposed steganographic scheme.

SABMIS: sparse approximation based blind multi-image steganography scheme

We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using the Alternating Direction Method of Multipliers (ADMM) to solve the Least Absolute Shrinkage and Selection Operator (LASSO) formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images. By choosing the size of the length and the width of the secret images to be half of the length and the width of cover image, respectively, we obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. Our focus is on hiding multiple secret images. For the case of hiding two and three secret images, our embedding capacities are higher than all the embedding capacities obtained in the literature until now (3 times and 6 times than the existing best, respectively). For the case of hiding four secret images, although our capacity is slightly lower than one work (about 2/3rd), we do better on the other two goals (quality of stego-image & extracted secret image as well as resistance to steganographic attacks). For our experiments, there is very little deterioration in the quality of the stego-images as compared to their corresponding cover images. Like all other competing works, this is supported visually as well as over 30 dB of Peak Signal-to-Noise Ratio (PSNR) values. The good quality of the stego-images is further validated by multiple numerical measures. None of the existing works perform this exhaustive validation. When using SABMIS, the quality of the extracted secret images is almost same as that of the corresponding original secret images. This aspect is also not demonstrated in all competing literature. SABMIS further improves the security of the inherently steganographic attack resistant transform based schemes. Thus, it is one of the most secure schemes among the existing ones. Additionally, we demonstrate that SABMIS executes in few minutes, and show its application on the real-life problems of securely transmitting medical images over the internet.

A novel steganography method for binary and color halftone images

Digital steganography is the science of establishing hidden communication on electronics; the aim is to transmit a secret message to a particular recipient using unsuspicious carriers such as digital images, documents, and audio files with the help of specific hiding methods. This article proposes a novel steganography method that can hide plaintext payloads on digital halftone images. The proposed method distributes the secret message over multiple output copies and scatters parts of the message randomly within each output copy for increased security. A payload extraction algorithm, where plain carrier is not required, is implemented and presented as well. Results gained from conducted objective and subjective tests prove that the proposed steganography method is secure and can hide large payloads.

Security of medical images for telemedicine: a systematic review

Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.

LSB-based pre-embedding video steganography with rotating & shifting poly-pattern block matrix

BACKGROUND

In terms of data-hiding areas, video steganography is more advantageous compared to other steganography techniques since it uses video as its cover medium. For any video steganography, the good trade-off among robustness, imperceptibility, and payload must be created and maintained. Even though it has the advantage of capacity, video steganography has the robustness problem especially regarding spatial domain is used to implement it. Transformation operations and statistical attacks can harm secret data. Thus, the ideal video steganography technique must provide high imperceptibility, high payload, and resistance towards visual, statistical and transformation-based steganalysis attacks.

METHODS

One of the most common spatial methods for hiding data within the cover medium is the Least Significant Bit (LSB) method. In this study, an LSB-based video steganography application that uses a poly-pattern key block matrix (KBM) as the key was proposed. The key is a 64 × 64 pixel block matrix that consists of 16 sub-pattern blocks with a pixel size of 16 × 16. To increase the security of the proposed approach, sub-patterns in the KBM are allowed to shift in four directions and rotate up to 270° depending on the user preference and logical operations. For additional security XOR and AND logical operations were used to determine whether to choose the next predetermined 64 × 64 pixel block or jump to another pixel block in the cover video frame to place a KBM to embed the secret data. The fact that the combination of variable KBM structure and logical operator for the secret data embedding distinguishes the proposed algorithm from previous video steganography studies conducted with LSB-based approaches.

RESULTS

Mean Squared Error (MSE), Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) parameters were calculated for the detection of the imperceptibility (or the resistance against visual attacks ) of the proposed algorithm. The proposed algorithm obtained the best MSE, SSIM and PSNR parameter values based on the secret message length as 0.00066, 0.99999, 80.01458 dB for 42.8 Kb of secret message and 0.00173, 0.99999, 75.72723 dB for 109 Kb of secret message, respectively. These results are better than the results of classic LSB and the studies conducted with LSB-based video steganography approaches in the literature. Since the proposed system allows an equal amount of data embedding in each video frame the data loss will be less in transformation operations. The lost data can be easily obtained from the entire text with natural language processing. The variable structure of the KBM, logical operators and extra security preventions makes the proposed system be more secure and complex. This increases the unpredictability and resistance against statistical attacks. Thus, the proposed method provides high imperceptibility and resistance towards visual, statistical and transformation-based attacks while acceptable even high payload.

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the "Ad-hoc Cloud System" idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

Double layer security using crypto-stego techniques: a comprehensive review

Recent advancement in the digital technology and internet has facilitated usage of multimedia objects for data communication. However, interchanging information through the internet raises several security concerns and needs to be addressed. Image steganography has gained huge attention from researchers for data security. Image steganography secures the data by imperceptibly embedding data bits into image pixels with a lesser probability of detection. Additionally, the encryption of data before embedding provides double-layer protection from the potential eavesdropper. Several steganography and cryptographic approaches have been developed so far to ensure data safety during transmission over a network. The purpose of this work is to succinctly review recent progress in the area of information security utilizing combination of cryptography and steganography (crypto-stego) methods for ensuring double layer security for covert communication. The paper highlights the pros and cons of the existing image steganography techniques and crypto-stego methods. Further, a detailed description of commonly using evaluations parameters for both steganography and cryptography, are given in this paper. Overall, this work is an attempt to create a better understanding of image steganography and its coupling with the encryption methods for developing state of art double layer security crypto-stego systems.

Sensitivity of deep learning applied to spatial image steganalysis

In recent years, the traditional approach to spatial image steganalysis has shifted to deep learning (DL) techniques, which have improved the detection accuracy while combining feature extraction and classification in a single model, usually a convolutional neural network (CNN). The main contribution from researchers in this area is new architectures that further improve detection accuracy. Nevertheless, the preprocessing and partition of the database influence the overall performance of the CNN. This paper presents the results achieved by novel steganalysis networks (Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net) using different combinations of image and filter normalization ranges, various database splits, different activation functions for the preprocessing stage, as well as an analysis on the activation maps and how to report accuracy. These results demonstrate how sensible steganalysis systems are to changes in any stage of the process, and how important it is for researchers in this field to register and report their work thoroughly. We also propose a set of recommendations for the design of experiments in steganalysis with DL.

Protection of the patient data against intentional attacks using a hybrid robust watermarking code

The security of patient information is important during the transfer of medical data. A hybrid spatial domain watermarking algorithm that includes encryption, integrity protection, and steganography is proposed to strengthen the information originality based on the authentication. The proposed algorithm checks whether the patient's information has been deliberately changed or not. The created code is distributed at every pixel of the medical image and not only in the regions of non-interest pixels, while the image details are still preserved. To enhance the security of the watermarking code, SHA-1 is used to get the initial key for the Symmetric Encryption Algorithm. The target of this approach is to preserve the content of the image and the watermark simultaneously, this is achieved by synthesizing an encrypted watermark from one of the components of the original image and not by embedding a watermark in the image. To evaluate the proposed code the Least Significant Bit (LSB), Bit2SB, and Bit3SB were used. The evaluation of the proposed code showed that the LSB is of better quality but overall the Bit2SB is better in its ability against the active attacks up to a size of 2*2 pixels, and it preserves the high image quality.

Minimal Block Knight's Tour and Edge with LSB Pixel Replacement Based Encrypted Image Steganography

The data security of an information is predominant in the digital world and gaining lot of importance. Cryptography and steganography are widely used in providing security to an information. In the proposed algorithm, the image encryption and steganography are performed using Knight's move in the game of chess called Knight's Tour Algorithm. Minimum block or square required for a knight's tour to reach all the squares is 5 × 5 block. The 5 × 5 blocks' pattern generated is used for image encryption. The encrypted image is then embedded into another image and block shuffling is performed to obtain a crypto-stego image. Proposed algorithm is robust and provides high data security with a good PSNR and SSIM.

Steganography in color images with random order of pixel selection and encrypted text message embedding

Information security is major concern in modern digital ages, and the outdated algorithms need to be replaced with new ones or to be improved. In this article a new approach for hiding secret text message in color images is presented, combining steganography and cryptography. The location and the order of the image pixels chosen for information embedding are randomly selected using chaotic pseudo-random generator. Encrypting the secret message before embedding is another level of security designed to misguide the attackers in case of analyzing for traces of steganography. Evaluating the proposed stegoalgorithm. The standard statistical and empirical tests are used for randomness tests, key-space analysis, key-sensitivity analysis, visual analysis, histogram analysis, peak signal-to-noise ratio analysis, chi-square analysis, etc. The obtained results are presented and explained in the present article.

PSO-Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture

Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 [COVID-19]) in efficient and secure transmission are important but challenging in communication channels amongst hospitals. In particular, in addressing the above challenges, two issues are faced, namely, those related to confidentiality and integrity of their health data and to network failure that may cause concerns about data availability. To the authors' knowledge, no study provides secure updating and sharing solution for large amounts of healthcare information in communication channels amongst hospitals. Therefore, this study proposes and discusses a novel steganography-based blockchain method in the spatial domain as a solution. The novelty of the proposed method is the removal and addition of new particles in the particle swarm optimisation (PSO) algorithm. In addition, hash function can hide secret medical COVID-19 data in hospital databases whilst providing confidentiality with high embedding capacity and high image quality. Moreover, stego images with hash data and blockchain technology are used in updating and sharing medical COVID-19 data between hospitals in the network to improve the level of confidentiality and protect the integrity of medical COVID-19 data in grey-scale images, achieve data availability if any connection failure occurs in a single point of the network and eliminate the central point (third party) in the network during transmission. The proposed method is discussed in three stages. Firstly, the pre-hiding stage estimates the embedding capacity of each host image. Secondly, the secret COVID-19 data hiding stage uses PSO algorithm and hash function. Thirdly, the transmission stage transfers the stego images based on blockchain technology and updates all nodes (hospitals) in the network. As proof of concept for the case study, the authors adopted the latest COVID-19 research published in the Computer Methods and Programs in Biomedicine journal, which presents a rescue framework within hospitals for the storage and transfusion of the best convalescent plasma to the most critical patients with COVID-19 on the basis of biological requirements. The validation and evaluation of the proposed method are discussed.

Steganography-based voice hiding in medical images of COVID-19 patients

A novel image steganography technique in order to hide the ciphered voice data has been suggested in this work. The doctor's voice comments belonging to a coronavirus disease 2019 (COVID-19) patient are hidden in a medical image in order to protect the patient information. The introduced steganography technique is based on chaos theory. Firstly, the voice comments of the doctor are converted to an image and secondly, they are ciphered utilizing the suggested encryption algorithm based on a chaotic system. Then, they are embedded into the cover medical image. A lung angiography dual-energy computed tomography (CT) scan of a COVID-19 patient is used as a cover object. Numerical and security analyses of steganography method have been performed in MATLAB environment. The similarity metrics are calculated for R, G, B components of cover image and stego image as visual quality analysis metrics to examine the performance of the introduced steganography procedure. For a 512 × 512 pixel cover image, SSIM values are obtained as 0.8337, 0.7926, and 0.9273 for R, G, B components, respectively. Moreover, security analyses which are differential attack, histogram, information entropy, correlation of neighboring pixels and the initial condition sensitivity are carried out. The information entropy is calculated as 7.9993 bits utilizing the suggested steganography scheme. The mean value of the ten UACI and NPCR values are obtained as 33.5688% and 99.8069%, respectively. The results of security analysis have revealed that the presented steganography procedure is able to resist statistical attacks and the chaotic system-based steganography scheme shows the characteristics of the sensitive dependence on the initial condition and the secret key. The proposed steganography method which is based on a chaotic system has superior performance in terms of being robust against differential attack and hiding encrypted voice comments of the doctor. Moreover, the introduced algorithm is also resistant against exhaustive, known plaintext, and chosen plaintext attacks.

ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks

Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection. Steganalysis is the counter-steganography domain which aims at detecting the existence of steganography within a host file. The presence of steganography in files raises suspicion regarding the file itself, as well as its origin and receiver, and might be an indication of a sophisticated attack. The JPEG file format is one of the most popular image file formats and thus is an attractive and commonly used carrier for steganography embedding. State-of-the-art JPEG steganalysis methods, which are mainly based on neural networks, are limited in their ability to detect sophisticated steganography use cases. In this paper, we propose ASSAF, a novel deep neural network architecture composed of a convolutional denoising autoencoder and a Siamese neural network, specially designed to detect steganography in JPEG images. We focus on detecting the J-UNIWARD method, which is one of the most sophisticated adaptive steganography methods used today. We evaluated our novel architecture using the BOSSBase dataset, which contains 10,000 JPEG images, in eight different use cases which combine different JPEG's quality factors and embedding rates (bpnzAC). Our results show that ASSAF can detect stenography with high accuracy rates, outperforming, in all eight use cases, the state-of-the-art steganalysis methods by 6% to 40%.

A new image steganography method with optimum pixel similarity for data hiding in medical images

Steganography is one of the approaches used in data hiding. Image steganography, is a type of steganography that the image is used as a covering object. Data hiding capacity and image quality of the cover object are important factors in image steganography. Because the deterioration of image quality can be noticed by the human vision system, it attracts the attention of attackers. Therefore, the purpose of this study is increasing the amount of data to be hidden and stego image is to ensure high image quality. In the study, a new optimization-based method has been proposed by making use of the similarities of the pixels. In order to test the performance of the proposed method has been used visual quality analysis metrics such as MSE, RMSE, PSNR, SSIM and UQI. As a cover object; different sizes medical images have been used that obtained from the open access Dicom library database. Doctor comments in different capacities have been hidden to the medical images. Experimental results show that the average PSNR value is 66.5374, 59.4420 and 56.3936, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 512 × 512 images. In addition, the average PSNR value is 60.4308, 53.3529 and 47.4113, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 256 × 256 images. 10,000 characters of data have not been hidden in 256 × 256 images without using data compression techniques with classical similarity based LSB method. In the proposed method, 10,000 characters of data have been hidden in 256 × 256 size images without using data compression techniques.

Photomontage detection using steganography technique based on a neural network

This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN method uses the characteristics of neural networks to create suitable attributes which are then necessary for subsequent detection of modified photographs. This also results in a fact that if an image is signed by this technique, the detection of modifications does not need any external data (database of non-modified originals) and the quality of the signature in various parts of the image also serves to identify modified (corrupted) parts of the image. The experimental study was performed on photographs from CoMoFoD Database and its results were compared with other approaches using this database based on standard metrics. The performed study showed the ability of the StegoNN method to detect corrupted parts of an image and to mark places which have been most probably image-manipulated. The usage of this method is suitable for reportage photography, but in general, for all cases where verification (provability) of authenticity and veracity of the presented image are required.

Based Medical Systems for Patient's Authentication: Towards a New Verification Secure Framework Using CIA Standard

In medical systems for patient's authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient's authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework.

Real-Time Medical Systems Based on Human Biometric Steganography: a Systematic Review

In real-time medical systems, the role of biometric technology is significant in authentication systems because it is used in verifying the identity of people through their biometric features. The biometric technology provides crucial properties for biometric features that can support the process of personal identification. The storage of biometric template within a central database makes it vulnerable to attack which can also occur during data transmission. Therefore, an alternative mechanism of protection becomes important to develop. On this basis, this study focuses on providing a detailed analysis of the extant literature (2013-2018) to identify the taxonomy and research distribution. Furthermore, this study also seeks to ascertain the challenges and motivations associated with biometric steganography in real-time medical systems to provide recommendations that can enhance the efficient use of real-time medical systems in biometric steganography and its applications. A review of articles on human biometric steganography in real-time medical systems obtained from three main databases (IEEE Xplore, ScienceDirect and Web of Science) is conducted according to an appropriate review protocol. Then, 41 related articles are selected by using exclusion and inclusion criteria. Majority of the studies reviewed had been conducted in the field of data-hiding (particularly steganography) technologies. In this review, various steganographic methods that have been applied in different human biometrics are investigated. Thereafter, these methods are categorised according to taxonomy, and the results are presented on the basis of human steganography biometric real-time medical systems, testing and evaluation methods, significance of use and applications and techniques. Finally, recommendations on how the challenges associated with data hiding can be addressed are provided to enhance the efficiency of using biometric information processed in any authentication real-time medical system. These recommendations are expected to be immensely helpful to developers, company users and researchers.

Preserving privacy of online digital physiological signals using blind and reversible steganography

BACKGROUND AND OBJECTIVE

Physiological signals such as electrocardiograms (ECG) and electromyograms (EMG) are widely used to diagnose diseases. Presently, the Internet offers numerous cloud storage services which enable digital physiological signals to be uploaded for convenient access and use. Numerous online databases of medical signals have been built. The data in them must be processed in a manner that preserves patients' confidentiality.

METHODS

A reversible error-correcting-coding strategy will be adopted to transform digital physiological signals into a new bit-stream that uses a matrix in which is embedded the Hamming code to pass secret messages or private information. The shared keys are the matrix and the version of the Hamming code.

RESULTS

An online open database, the MIT-BIH arrhythmia database, was used to test the proposed algorithms. The time-complexity, capacity and robustness are evaluated. Comparisons of several evaluations subject to related work are also proposed.

CONCLUSIONS

This work proposes a reversible, low-payload steganographic scheme for preserving the privacy of physiological signals. An (n,  m)-hamming code is used to insert (n - m) secret bits into n bits of a cover signal. The number of embedded bits per modification is higher than in comparable methods, and the computational power is efficient and the scheme is secure. Unlike other Hamming-code based schemes, the proposed scheme is both reversible and blind.

Improved diagonal queue medical image steganography using Chaos theory, LFSR, and Rabin cryptosystem

In this article, we have proposed an improved diagonal queue medical image steganography for patient secret medical data transmission using chaotic standard map, linear feedback shift register, and Rabin cryptosystem, for improvement of previous technique (Jain and Lenka in Springer Brain Inform 3:39–51, 2016). The proposed algorithm comprises four stages, generation of pseudo-random sequences (pseudo-random sequences are generated by linear feedback shift register and standard chaotic map), permutation and XORing using pseudo-random sequences, encryption using Rabin cryptosystem, and steganography using the improved diagonal queues. Security analysis has been carried out. Performance analysis is observed using MSE, PSNR, maximum embedding capacity, as well as by histogram analysis between various Brain disease stego and cover images.

Dual-Level Security based Cyclic18 Steganographic Method and its Application for Secure Transmission of Keyframes during Wireless Capsule Endoscopy

In this paper, the problem of secure transmission of sensitive contents over the public network Internet is addressed by proposing a novel data hiding method in encrypted images with dual-level security. The secret information is divided into three blocks using a specific pattern, followed by an encryption mechanism based on the three-level encryption algorithm (TLEA). The input image is scrambled using a secret key, and the encrypted sub-message blocks are then embedded in the scrambled image by cyclic18 least significant bit (LSB) substitution method, utilizing LSBs and intermediate LSB planes. Furthermore, the cover image and its planes are rotated at different angles using a secret key prior to embedding, deceiving the attacker during data extraction. The usage of message blocks division, TLEA, image scrambling, and the cyclic18 LSB method results in an advanced security system, maintaining the visual transparency of resultant images and increasing the security of embedded data. In addition, employing various secret keys for image scrambling, data encryption, and data hiding using the cyclic18 LSB method makes the data recovery comparatively more challenging for attackers. Experimental results not only validate the effectiveness of the proposed framework in terms of visual quality and security compared to other state-of-the-art methods, but also suggest its feasibility for secure transmission of diagnostically important keyframes to healthcare centers and gastroenterologists during wireless capsule endoscopy.

Diagonal queue medical image steganography with Rabin cryptosystem

The main purpose of this work is to provide a novel and efficient method to the image steganography area of research in the field of biomedical, so that the security can be given to the very precious and confidential sensitive data of the patient and at the same time with the implication of the highly reliable algorithms will explode the high security to the precious brain information from the intruders. The patient information such as patient medical records with personal identification information of patients can be stored in both storage and transmission. This paper describes a novel methodology for hiding medical records like HIV reports, baby girl fetus, and patient’s identity information inside their Brain disease medical image files viz. scan image or MRI image using the notion of obscurity with respect to a diagonal queue least significant bit substitution. Data structure queue plays a dynamic role in resource sharing between multiple communication parties and when secret medical data are transferred asynchronously (secret medical data not necessarily received at the same rate they were sent). Rabin cryptosystem is used for secret medical data writing, since it is computationally secure against a chosen-plaintext attack and shows the difficulty of integer factoring. The outcome of the cryptosystem is organized in various blocks and equally distributed sub-blocks. In steganography process, various Brain disease cover images are organized into various blocks of diagonal queues. The secret cipher blocks and sub-blocks are assigned dynamically to selected diagonal queues for embedding. The receiver gets four values of medical data plaintext corresponding to one ciphertext, so only authorized receiver can identify the correct medical data. Performance analysis was conducted using MSE, PSNR, maximum embedding capacity as well as by histogram analysis between various Brain disease stego and cover images.

'Yeast mail': a novel Saccharomyces application (NSA) to encrypt messages

The universal genetic code is used by all life forms to encode biological information. It can also be used to encrypt semantic messages and convey them within organisms without anyone but the sender and recipient knowing, i.e., as a means of steganography. Several theoretical, but comparatively few experimental, approaches have been dedicated to this subject, so far. Here, we describe an experimental system to stably integrate encrypted messages within the yeast genome using a polymerase chain reaction (PCR)-based, one-step homologous recombination system. Thus, DNA sequences encoding alphabetical and/or numerical information will be inherited by yeast propagation and can be sent in the form of dried yeast. Moreover, due to the availability of triple shuttle vectors, Saccharomyces cerevisiae can also be used as an intermediate construction device for transfer of information to either Drosophila or mammalian cells as steganographic containers. Besides its classical use in alcoholic fermentation and its modern use for heterologous gene expression, we here show that baker's yeast can thus be employed in a novel Saccharomyces application (NSA) as a simple steganographic container to hide and convey messages.