Steganography Approach Research Articles

МЕТОД АДАПТИВНОГО БАГАТОШАРОВОГО ЗАХИСТУ ІНФОРМАЦІЇ НА ОСНОВІ СТЕГАНОГРАФІЇ ТА КРИПТОГРАФІЇ

An analysis of known solutions to the problem of steganographic and cryptographic methods combining to achieve multilayer information protection is presented. Results of the analysis allowed us to identify trends and prospects for the development of these kink of methods. Based on the results of the analysis, the research task was determined concerning the adaptation of the used cryptographic and steganographic methods in order to achieve the best level of protection, which is needed at critical systems. Metrics were defined, those allowed to choose the best combination of cryptographic and steganographic protection methods parameters. A method of multilayered information protection is presented, which combines cryptographic and steganographic approaches to ensure an increased level of information’s confidentiality and integrity protection. It is proposed to introduce criteria for the selection of cryptographic transformations in such a way that their combination together with steganographic transformations gives the best impact. An instance of the proposed method implementation is given in order to prove the concept. An algorithm has been developed that implements the proposed method of adaptive multilayer information protection. The perspective of further research is determined.

Interpolated Implicit Pixel-based Novel Hybrid Approach Towards Image Steganography

Background: Steganography is the approach of camouflaging the covert object within another cover object. This manuscript suggested a novel steganography approach to conceal the covert data presence. The basic idea behind this is to generate an information-hiding approach that increases the payload capacity and good PSNR value without sacrificing much distortion of the image. Objectives: To develop a novel data-hiding approach that increases imperceptibility, robustness, and payload capacity. Methods: The Neighbour Mean Interpolation technique is used to scale up the original image to generate Interpolated pixels of the given image. An even-odd scheme on the interpolated stego pixel is used to camouflage the obscure code. MATLAB is used for the implementation of the new approach and results calculation. Results: The Experimental analysis reveals that our suggested approach has a finer embedding capacity for camouflaging the secret data as the original image of size (MxN) is scaled up to size (2M- 1 x2N-1) and also manages the good visuality of the cover or graven image. The proposed method is compared with Jung and Yoo, and Selvrani's method. The result of this comparison shows that the proposed method has finer imperceptibility than these two previously existing techniques. Conclusion: A novel approach towards image steganography using neighbor mean interpolation has been proposed and implemented. A new steganography method is used for camouflaging the confidential code into the cover object using NMI without producing any major differences in the input image. The new approach provides better imperceptibility, robustness, and payload capacity.

Matrix Inverse-based Highly Payload Novel Approach for Covert Transmission

Background: The necessity for data security is demanding in the digital age. The main technique for achieving data security is Steganography. The technique of camouflaging the secret object behind another cover object is known as Steganography. A novel, robust, high payload, and imperceptible image steganography approach using matrix inverse in the spatial domain are proposed in this manuscript. The basic idea is to devise a robust novel approach against various image processing attacks, like cropping, compression, filters, and noise. Objectives: The study's objective is to develop a novel data-hiding approach that increases imperceptibility and payload capacity and is robust against various image processing attacks, like filters, compression, cropping, and noise. Methods: The matrix inverse procedure is used for the insertion and extraction of data. The symmetry feature of the matrix inverse makes the task of insertion of data simple and efficient. It also increases the hiding capacity while maintaining a finer level of imperceptibility and robustness. MATLAB is used for the implementation of the new technique and results analysis. Results: The proposed method's robustness has been analyzed against image processing assaults such as the inclusion of various noises, cropping, a variety of filters, and compression assaults. The imperceptibility of the approach has been tested successfully using PSNR, BER, and NCC metrics. The proposed method has been compared with the other two techniques. The experimental and comparison results depict that the proposed approach provides high hiding capacity, finer robustness, and imperceptibility. Conclusion: A novel, robust and imperceptible approach has been developed in this manuscript. The proposed method has been compared with the methods developed by Jung & Yoo and Joshi & Gill. The experimental findings show that the proposed technique offers better resilience, payload capacity, and finer imperceptibility.

Video and Image Steganography

Video steganography, a technique for embedding secret information within video sequences, has gained prominence in the domain of covert communication. By exploiting the inherent redundancy and high capacity of video data, this approach enables secure data transmission without raising suspicion. This paper delves into the intricacies of AES-based video steganography, with a particular focus on the integration of the Advanced Encryption Standard (AES) algorithm. The AES algorithm, renowned for its robust encryption capabilities, serves as a cornerstone of this steganographic approach. By encrypting the secret data prior to embedding it within the video frames, an additional layer of security is introduced, safeguarding the confidentiality of the hidden information

ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research.

An Android-based Image Steganography Approach to Data Communication Security using LSB and Password-based Encryption

The study's ultimate goal is to develop an Android app dedicated to Image Steganography, a technique for concealing sensitive information behind seemingly innocuous photographs. This information hiding technique is essential because of its many potential uses. Symmetric encryption, asymmetric encryption, and steganography are all brought together in this method. The initial picture is encrypted using a symmetric technique in this procedure. After that, the encrypted picture is quietly placed using a least significant bits Steganographic approach to conceal the secret key that was encrypted using an asymmetric algorithm. The steganography algorithm's simple but effective security mechanism is a major benefit. Integrating a secret message into a seemingly benign source makes it very difficult to detect the hidden message without prior knowledge of its existence and the appropriate decryption algorithm. The privacy of the concealed data is protected by this feature. The planned Android app would provide a user-friendly interface for using picture steganography methods, making it simple for anybody to choose commonplace photos and secret messages for obfuscation. Both the original picture and the concealed message will be kept secret thanks to the app's robust encryption methods. The hidden message will be seamlessly included into the regular picture using advanced steganographic methods like the least significant bits approach. The creation of this software is motivated by a desire to provide an easily accessible solution for people who want safe communication through hidden messages or encrypted photos. The robust encryption and steganographic technologies, paired with the straightforward interface, will enable users to effectively protect sensitive information. This work will strengthen the area of data security and highlight the need of sophisticated encryption and steganography in modern digital communication.

An Enhanced Steganography Approach for Concealing Audio in Images Using Double Density-Dual Tree Wavelet Transform

An Enhanced Steganography Approach for Concealing Audio in Images Using Double Density-Dual Tree Wavelet Transform

Deep-learning-assisted optical steganographic encryption via ghost encoding and binary hologram

We propose a steganographic approach for encrypting optical images by integrating ghost encoding and computer-generated holography. The process involves encoding a secret image using ghost imaging during its collection and subsequently encrypting it into a binary amplitude-only hologram (BAOH) with steganographic characteristics. The resultant BAOH servers as the ciphertext and can be printed out on a polyethylene terephthalate film using an inkjet printer. To enhance flexibility and reconstruction quality, we incorporate a deep neural network driven by sparse and noisy data to work alongside into the cryptosystem. Both the pure digital hologram cyphertext and its physical representation can be successfully decrypted. Experimental results confirm the feasibility of our scheme, offering benefits such as easy-to-carry storage, low production costs, and high security.

Robust Image Steganography Based on Hybrid Edge Detection

Steganography is a technique for hiding digital messages into other digital media, such as images, audio, video, and others so that the messages can be disguised and not directly visible to the human vision. There are two kinds of steganography by domain, i.e., spatial domain and frequency domain [7]. Least Significant Bit (LSB) is a popular method in spatial domains [8]. LSB is not a new method. However, this method has many advantages, such as simple algorithm and the quality of imperceptibility of relatively good stego-image [6, 9]. This makes the LSB still open for more development and further research. This paper presents the image hiding technique using Least Significant Bit (LSB) steganography based on hybrid edge detection that maximized the data embedding capacity of the cover image. The performance of the system is evaluated for hybrid edge detection using pairs of various edge detection techniques such as Canny, Kirsch, Sobel, and Prewitt. The results of the proposed steganography approach are estimated for different payload capacities based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), and Universal Image Quality Index (UQI). It is examined that the proposed approach performs better than the existing state of arts for image steganography.

High performance image steganography integrating IWT and Hamming code within secret sharing

AbstractSteganography and secret sharing are schemes to increase the security of private information against attackers. Steganography emphasizes secrecy while secret sharing distributes the secret key in shares that are conditionally classified to reconstruct the original secret. This paper introduces a counting‐based secret sharing scheme that aims to reduce the computational complexity for longer keys, thereby providing a practical steganographic approach for efficient sharing. The scheme integrates counting‐based secret sharing with integer wavelet transform (IWT) and steganography. The subscriptions created using the Hamming code are embedded in the cover image. Using this method, IWT significantly reduces the occurrence of common rounding errors. As a result, secret key extraction becomes very accurate and eliminates the need to access the original images. This design, using secret sharing based on counting, not only has simplicity and efficiency, but also features such as flexibility, scalability, and lack of central authority. In addition, high‐quality steganography was obtained for keys with lengths of 64, 256, 512, 1024, and 3072, showing an average PSNR=80.11 in different color images. This outstanding performance makes it a highly efficient alternative to previous designs, representing a groundbreaking contribution with significant public interest in the field.

A Secure and High Capacity Image Steganography Approach Using Huffman Coding and RSA Encryption

Steganography is the practice of hiding data, such as images, videos, or text, within a cover image without it being detectable by the human eye. Several factors, such as the capacity, security, and robustness of the technique, are essential when transferring information using this method. In this study, we propose a new approach to image steganography that improves the Least Significant Bit (LSB) technique by utilizing images of 24 bits in each pixel. Improving the capacity and security of LSB-based steganography requires a combination of techniques, such as indirect embedding, embedding in multiple channels, and applying cryptographic and compression techniques. This approach conducts by encrypting each compressed secret message bit with the most significant bit of the red channel and then saving the output bit (hidden bit) in the least significant bit of the (green/blue) channel according to the row value (odd/ even). To further security, the suggested approach uses multi-level encryption; employs RSA to encrypt the secret message before applying the Huffman compression and encrypting the hidden bit by (XOR/XNOR) in the embedding method based on the row value of the pixel. Meanwhile, it is planned to use the Huffman coding technique to shorten the length of the encrypted message that will be inserted in the cover image. For the color photos in this work, the standard images were acquired from a standard dataset (USC-SIPI). The suggested approach performs better when measured in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), and comparison to findings from related prior efforts.

Secret-to-Image Reversible Transformation for Generative Steganography

Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent space with multivariate Gaussian distribution and image space with a complex distribution. In the process of S2I transformation, guided by a given secret message, we construct a latent vector and then map it to a generated image by the Glow model, so that the secret message is finally transformed to the generated image. Owing to good efficiency and reversibility of S2IRT scheme, the proposed steganographic approach achieves both high hiding capacity and accurate extraction of secret message from generated image. Furthermore, a separate encoding-based S2IRT (SE-S2IRT) scheme is also proposed to improve the robustness to common image attacks. The experiments demonstrate the proposed steganographic approaches can achieve high hiding capacity (up to 4 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bpp</i> ) and accurate information extraction (almost 100% accuracy rate) simultaneously, while maintaining desirable anti-detectability and imperceptibility.

A Blind Image Steganography Algorithm Based on Knight Tour Algorithm and QR Codes

Internet proliferation and technological progress have made multimedia information quickly accessible, but they have also posed a threat to privacy and security. Researchers have been interested in digital images due to their capacity to store large amounts of data due to the possibility of protecting sensitive information through digital steganography. Despite their visual imperceptibility, robustness, and ability to embed information, existing image steganography techniques face several challenges. To overcome these challenges, a novel image steganography approach based on a blind model strategy has been proposed for hiding covert messages. The model consists of two stages: embedding and extracting. In the embedding stage, a suitable cover image is selected using the FAST feature point detector. A text message is then converted to a QR code and embedded in the feature points' neighbors using knight tour steps in a chess game. The result is a stego image that appears identical to the original cover image but contains the secret message in its feature points' neighbors. The extracting stage involves finding the feature points and extracting the QR code to obtain the original text message. Since the feature points were not altered during the embedding process, the proposed model is known as a blind model. This approach eliminates the need for the original cover image during the extracting stage. The proposed model was evaluated using several metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results demonstrate that the proposed algorithm can effectively embed and extract secret messages with high accuracy while maintaining the visual quality of the cover image with 100% of (SSIM), and 73.48 as an average of (PSNR).

A cooperative three-player game theory approach for designing an ideal video steganography framework

ABSTRACT This paper presents a cooperative game theory approach to improve the video steganography framework. Wherein, the video steganography framework comprises the following steps: (1) Cover video devising, (2) Secret image pre-processing, and (3) Embedding process. In the first step, the cover video is segmented using scene change detection method. Once the scenes are segmented the motion vectors are identified by Block Matching Motion Estimation Algorithm (BMMEA). Based on these motion vectors, the Region of Interest (ROI) is selected. The selected ROI is grouped using their momentum. In order to add an additional layer of security, the secret image is scrambled using pixel-wise Arnold Transform in the next step. Finally, based on the ROI groups, the scrambled secret data is embedded into the ROI's locations. An ideal and perfect video steganography system should be able to maintain the quality of the video after embedding the secret data. The quality factors of the video steganography approach include perceptible invisibility, payload capacity, and robustness. However, these quality factors are conflicted with each other. In order to address this issue, having these three quality factors as players a cooperative 3-players game theory approach is proposed. This technique provides an optimal solution for the video steganography framework using strategy adaption. The optimal solution is acquired by applying the Iterative Elimination of Strictly Dominant Strategies (IESDS) method. The achieved optimal solution comes up with the best trade-off between the quality factors. Experimental results have deduced the best optimal solution from the proposed three-player game theoretic approach that helps the video steganography approach to resolve the quality and security issues. It is also observed that the optimized proposed method outperforms the contemporary methods by attaining significant outcomes.

A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters’ Diacritics and Image Layers

Steganography is a widely used technique for concealing confidential data within images, videos, and audio. However, using text for steganography has not been sufficiently explored. Text-based steganography has the advantage of a low bandwidth overhead, making it a promising alternative for protecting sensitive information. Among languages, Arabic is known for its linguistic richness, making it ideal for text-based steganography. This paper proposes a robust, dynamic, and multi-layered steganography approach that uses text, encryption algorithms, and images. This approach utilizes Arabic diacritic features to hide limited-size and highly classified information. The algorithm uses several scenarios and is extensively tested to ensure the required level of security and user performance. The experimental results on actual data demonstrate the robustness of the proposed algorithm, with no noticeable impact on the carrier message (original text). Furthermore, no known potential attack can break the proposed algorithm, making it a promising solution for text-based steganography.

Toward secret data location via fuzzy logic and convolutional neural network

Locating hidden data in digital images, otherwise called steganalysis, is a process of identifying the existence of secret messages within digital images. Steganalysis is used to manage digital data transmission by detecting the possible hidden information that can be used to violate the network policy; hence, it helps the development of policies and regulations aimed at strong protection from cyber threats to individuals’ and organizations’ data. The research works in the field of information security commonly focus on developing the locating approaches for non-adaptive steganography, which present a problem of less investigation of the complex challenge of locating the payload embedded with an adaptive steganographic algorithm. In this article, we propose a method to locate hidden data in a digital image in three stages: a) Identification of the modification maps between the carrier and final images. b) Using the modification maps as input to Mamdani fuzzy inference with four input membership functions: covariance map matrix, compass mean matrix, distance vector matrix, and pixel intensity matrix, and one output membership function, notably the fuzzy correlation maps. c) Feeding the fuzzy correlation maps to a convolutional neural network to identify the pixels with confidential data from the innocent pixels. By experimenting with our method against four steganographic algorithms, namely, HILL, HUGO-BD, WOW, and S-UNIWARD, the recall rates for the four algorithms initially increase in a similar range and improve with increasing payload capacity, which justifies the outperformance of the proposed strategy over the existing methods.

LM-GA: A Novel IDS with AES and Machine Learning Architecture for Enhanced Cloud Storage Security

Cloud Computing (CC) is a relatively new technology that allows for widespread access and storage on the internet. Despite its low cost and numerous benefits, cloud technology still confronts several obstacles, including data loss, quality concerns, and data security like recurring hacking. The security of data stored in the cloud has become a major worry for both Cloud Service Providers (CSPs) and users. As a result, a powerful Intrusion Detection System (IDS) must be set up to detect and prevent possible cloud threats at an early stage. Intending to develop a novel IDS system, this paper introduces a new optimization concept named Lion Mutated-Genetic Algorithm (LM-GA) with the hybridization of Machine Learning (ML) algorithms such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Initially, the input text data is preprocessed and balanced to avoid redundancy and vague data. The preprocessed data is then subjected to the hybrid Deep Learning (DL) models namely the CNN-LSTM model to get the IDS output. Now, the intruded are discarded and non-intruded data are secured using Advanced Encryption Standard (AES) encryption model. Besides, the optimal key selection is done by the proposed LM-GA model and the cipher text is further secured via the steganography approach. NSL-KDD and UNSW-NB15 are the datasets used to verify the performance of the proposed LM-GA-based IDS in terms of average intrusion detection rate, accuracy, precision, recall, and F-Score.

Video steganography: recent advances and challenges

Video steganography approach enables hiding chunks of secret information inside video sequences. The features of video sequences including high capacity as well as complex structure make them more preferable for choosing as cover media over other media such as image, text, or audio. Video steganography is a prominent as well as the evolving field in the information security domain and significant number of video steganography methods are proposed in recent years. This article provides a comprehensive review of video steganography methods proposed in the literature. This article initially reviews various raw domain-based video steganography methods. In particular, the raw domain-based methods include spatial domain approaches such as least significant bits (LSB), transform domain-based methods such as discrete wavelet transform, discrete cosine transform, etc. Furthermore, the article looks into various compressed domain steganography methods. A critical comparative analysis is included in the article to analyze and contrast the steganography methods proposed in the literature. A brief description of various evaluation matrices for video steganography methods is provided in this article. Moreover, a brief introduction to steganalysis and video steganalysis is provided. The article concludes with a discussion focused on the limitations and challenges of the video steganography methods. Further, a brief insight into future directions in video steganography systems is provided.

An adaptive image steganography approach depending on integer wavelet transform and genetic algorithm

An adaptive image steganography approach depending on integer wavelet transform and genetic algorithm

Unprecedented Security Analysis Results for a Novel Steganography Approach Based on Protein Sequences

Unprecedented Security Analysis Results for a Novel Steganography Approach Based on Protein Sequences